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Estimating tree leaf biomass can be challenging in applications where predictions for multiple tree species is required. This is especially evident where there is limited or no data available for some of the species of interest. Here we use an extensive national database of observations (61 species, 3628 trees) and formulate models of varying complexity, ranging from a simple model with diameter at breast height (DBH) as the only predictor to more complex models with up to 8 predictors (DBH, leaf longevity, live crown ratio, wood specific gravity, shade tolerance, mean annual temperature, and mean annual precipitation), to estimate tree leaf biomass for any species across the continental United States. The most complex with all eight predictors was the best and explained 74%–86% of the variation in leaf mass. Consideration was given to the difficulty of measuring all of these predictor variables for model application, but many are easily obtained or already widely collected. Because most of the model variables are independent of species and key species‐level variables are available from published values, our results show that leaf biomass can be estimated for new species not included in the data used to fit the model. The latter assertion was evaluated using a novel "leave‐one‐species‐out" cross‐validation approach, which showed that our chosen model performs similarly for species used to calibrate the model, as well as those not used to develop it. The models exhibited a strong bias toward overestimation for a relatively small subset of the trees. Despite these limitations, the models presented here can provide leaf biomass estimates for multiple species over large spatial scales and can be applied to new species or species with limited leaf biomass data available. [ABSTRACT FROM AUTHOR]

The Brazilian Atlantic Forest is a biologically and structurally diverse biome which covers a large area; however, few tree-level biomass models exist for this region. The objective of this study was to evaluate alternative individual tree biomass models for the Atlantic Forest using contrasting prediction approaches, and evaluate their implications for stand-level biomass and carbon. The data comprised 106 destructively sampled trees (dbh; 5.4 - 68.5 cm) from the 16 most abundant species. Different approaches, namely species-specific, functional-trait based, generalized, and previously developed equations, as well as alternative model forms using differing variables including diameter at breast height (dbh), wood specific gravity (wsg), and height to crown base (hcb) were examined. Species-specific models were developed using both ordinary least squares and linear mixed effects with species as a random effect. The various approaches resulted in statistically different estimates of biomass and carbon at both the individual tree- and stand-levels. Compared to a generalized biomass equation for Pan-tropical forests, our results indicate differences of over 46% and 52% for biomass and carbon, respectively. Overall, if no species-specific model is available, we suggest the functional plant trait based on dbh and wsg. [ABSTRACT FROM AUTHOR]

Measures of forest productivity generally rely on site index, which can be problematic for multicohort and mixed-species stands. Using stand growth and dominant tree height-age (i.e., site tree) measurements from ∼10 900 plot locations from Maine, Nova Scotia, New Brunswick, and Prince Edward Island, a forest productivity model for the Acadian Forest Region was developed as a function of climate, lithology, soils, and topographic metrics. Approximately 65% of variation in observed aboveground dry-biomass growth rate (BG) was explained by a Chapman-Richards function of temperature, bedrock, soil root space, slope, and depth to water in combination with stand structure and species predictors. Productivity was then defined in terms of the predicted asymptote of BG, holding structure and species constant, which was termed biomass growth index (BGI), i.e., the site-influenced component of the BG relationship. BGI was mapped on a 20 m grid throughout the region. BGI explained 0%-30% of the variability in spruce ( Picea sp.) and balsam fir ( Abies balsamea (L.) Mill.) site index and had similar site index predictive performance (±5%) when compared with existing land productivity classifications in each province. BGI provides a direct relationship between site variables and growth and can help guide forest management decisions and future research. [ABSTRACT FROM AUTHOR]

Dead wood pools are strongly influenced by natural disturbance events, stand development processes, and forest management activities. However, the relative importance of these influences can vary over time. In this study, we evaluate the role of these factors on dead wood biomass pools across several forest management alternatives after 60 years of treatment on the Penobscot Experimental Forest in central Maine, USA. After accounting for variation in site quality, we found significant differences in observed downed coarse woody material (CWM; ≥7.6 cm small-end diameter) and standing dead wood biomass among selection, shelterwood, and commercial clear-cut treatments. Overall, total dead wood biomass was positively correlated with live tree biomass and was negatively correlated with the average wood density of nonharvest mortality. We also developed an index of cumulative harvest severity, which can be used to evaluate forest attributes when multiple harvests have occurred within the same stand over time. Findings of this study highlight the dynamic roles of forest management, stand development, and site quality in influencing dead wood biomass pools at the stand level and underscore the potential for various outcomes from the same forest management treatment applied at different times in contrasting stands. [ABSTRACT FROM AUTHOR]

Tree biomass is typically estimated using statistical models. This review highlights five limitations of most tree biomass models, which include the following: (1) biomass data are costly to collect and alternative sampling methods are used; (2) belowground data and models are generally lacking; (3) models are often developed from small and geographically limited data sets; (4) simplistic model forms and predictor variables are used; and (5) variation is commonly averaged or grouped rather than accounted for. The consequences of these limitations are highlighted and discussed. Several recommendations for future efforts are presented including the following: (1) collection of field measurements of tree biomass using consistent protocols; (2) compilation of existing data; (3) continued evaluation and improvement of existing models; (4) exploration of new models; and (5) adoption of state-of-the-art analytical and statistical techniques. Given the increasing importance of accurately estimating forest biomass, there is a critical need to understand, evaluate, and improve current tree biomass prediction methods. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR) data have become almost ubiquitous as a remote sensing tool in forestry estimation and mapping applications. Such initiatives commonly rely on spatially aligned forest inventory plot measurements and LiDAR covariates to inform model-based estimators for small area estimation. There are many examples where such linking models provide the desired accuracy and precision of forest parameter estimates for small areas where paucity of inventory plot observations preclude design-based inference. This paper builds on previous small area estimation (SAE) work by linking LiDAR covariates with variable radius forest inventory plot measurements within a hierarchical Bayesian framework. Using this framework, we compare SAE of forest aboveground biomass using: i ) Fay-Herriot (FH); ii ) FH with conditional autoregressive random effects (FHCAR); and iii ) FHCAR with smoothed sampling variance (FHCAR-SMOOTH) models. Candidate models and the direct estimate based on plot measurements alone were compared using coefficient of variation (CV). On average, the FH model reduced the CV by 52.3% compared to the direct estimate. Incorporating spatial structure via the FHCAR model reduced the CV by 56.9% and 10.8% relative to the direct and the FH model estimates, respectively. Overall, these results illustrate the applicability and utility of using a SAE framework for linking LiDAR with typical forest inventory data. [ABSTRACT FROM AUTHOR]

Combining spatially-explicit long-term forest inventory and remotely sensed information from Light Detection and Ranging (LiDAR) datasets through statistical models can be a powerful tool for predicting and mapping above-ground biomass (AGB) at a range of geographic scales. We present and examine a novel modeling approach to improve prediction of AGB and estimate AGB growth using LiDAR data. The proposed model accommodates temporal misalignment between field measurements and remotely sensed data—a problem pervasive in such settings—by including multiple time-indexed measurements at plot locations to estimate AGB growth. We pursue a Bayesian modeling framework that allows for appropriately complex parameter associations and uncertainty propagation through to prediction. Specifically, we identify a space-varying coefficients model to predict and map AGB and its associated growth simultaneously. The proposed model is assessed using LiDAR data acquired from NASA Goddard's LiDAR, Hyper-spectral & Thermal imager and field inventory data from the Penobscot Experimental Forest in Bradley, Maine. The proposed model outperformed the time-invariant counterpart models in predictive performance as indicated by a substantial reduction in root mean squared error. The proposed model adequately accounts for temporal misalignment through the estimation of forest AGB growth and accommodates residual spatial dependence. Results from this analysis suggest that future AGB models informed using remotely sensed data, such as LiDAR, may be improved by adapting traditional modeling frameworks to account for temporal misalignment and spatial dependence using random effects. [ABSTRACT FROM AUTHOR]