The stem height–diameter allometric relationship is fundamental in determining forest and ecosystem structures as well as in estimating tree volume, biomass, and carbon stocks. Understanding the effects of silvicultural practices on tree height–diameter allometry is necessary for sustainable forest management, though the impact of measures such as thinning on the allometric relationship remain understudied. In the present study, the effects of thinning on tree height–diameter allometry were evaluated using Masson pine height and diameter growth data from a plantation experiment that included unthinned and thinned treatments with different intensities. To determine whether thinning altered the height–diameter allometry rhythm, the optimal height–diameter model was identified and dummy variable methods were used to investigate the differences among model parameters for different thinning treatments. Periodic (annual) allometric coefficients were calculated based on height and diameter increment data and were modeled using the generalized additive mixed model (GAMM) to further illustrate the response of tree height–diameter allometry to different thinning treatments over time. Significant differences were detected among the parameters of the optimal height–diameter model (power function) for different thinning treatments, which indicated that the pattern of the height–diameter allometry relationship of Masson pine was indeed altered by thinning treatments. Results also indicated a nonlinear trend in the allometric relationship through time which was significantly affected by thinning. The height–diameter allometric coefficient exhibited a unimodal convex bell curve with time in unthinned plots, and thinning significantly interfered with the original trend of the height–diameter allometric coefficient. Thinning caused trees to increase diameter growth at the expense of height growth, resulting in a decrease of the ratio of tree height to diameter, and this trend was more obvious as the thinning intensity increased.

We subjected the individual-tree, aspatial basal area increment model developed for the Inland Empire Variant of the Forest Vegetation Simulator to validation and evaluation tests. We used a large set of independent data from the Forest Inventory and Analysis program that covers the geographic extent to which the model is usually applied. Equivalence tests did not validate the model as a predictive tool using nominated criteria, though they usually did validate the model structure as a theory. Design-unbiased estimates of prediction error suggest that the model overpredicts diameter and volume increment by 14% and 2%, respectively. Relationships between species, bias, and predictor variables suggest the model may overpredict most on productive sites. We spatially interpolated the model performance across the study area using thin-plate splines. The observed regional patterns are examined using a selection of cross-sectional transects, and reveal a complex relationship between bias and the way climate effects are incorporated in the model structure that involve differences between the fitting and testing data. The model structure is surprisingly robust, but the representation of climate effects should be a priority in future revisions.

The conventional approach to LiDAR-based forest inventory modeling depends on field sample data from fixed-radius plots (FRP). Because FRP sampling is cost intensive, combining variable-radius plot (VRP) sampling and LiDAR data has the potential to improve inventory efficiency. The overarching goal of this study was to evaluate the integration of LiDAR and VRP data. FRP and VRP plots using different basal-area factors (BAF) were colocated in 6 conifer stands near Alberta, Michigan, in the United States. A suite of LiDAR metrics was developed for 24 different resolutions at each plot location, and a number of nonparametric prediction models were evaluated to identify an optimal LiDAR resolution and an optimal scale of VRP to spatially extend the data. An FRP-based model had root mean square error (RMSE) of 31.8 m³ ha⁻¹, whereas the top VRP-based models were somewhat less precise, with RMSE of 38.0 m³ ha⁻¹ and 45.8 m³ ha⁻¹ using BAF 2.06 m² ha⁻¹ and BAF 2.29 m² ha⁻¹, respectively. The optimal LiDAR resolution for the VRP data was found to be 18 m for the selected stands, and plot-level estimates based on a model using BAF 2.29 m² ha⁻¹ were statistically equivalent to the FRP measurements. The use of VRP data shows promise and can substitute for FRP measurements to improve efficiency.

The selection silvicultural system is widely recommended for sustainable management of North American cool-temperate tolerant hardwood forests, yet concerns abo

The positive effect of thinning on individual tree growth is well known, but the subsequent growth dynamics of individual trees over a rotation is relatively unknown, even though this is critical for interpreting actual thinning effects. Therefore, in this study, we aimed to investigate the thinning response of individual tree growth dynamics of lodgepole pine (Pinus contorta var. latifolia) over rotation using an individual tree growth model. We used data from an operational site to use as input to the Mixedwood Growth Model, an individual tree growth model, to simulate tree growth throughout the rotation. Overall, we found that thinning increased the absolute growth of individual trees and reduced the growth heterogeneity throughout the rotation. Combining pre-commercial thinning prior to commercial thinning provided maximum growth and less growth variability in individual trees. The positive effect of thinning was immediate and declined with time since thinning with most of the responses occurred within the first 10–15 years of thinning.

Protecting the future of forests in the United States and other countries depends in part on our ability to monitor and map forest health conditions in a timely fashion to facilitate management of emerging threats and disturbances over a multitude of spatial scales. Remote sensing data and technologies have contributed to our ability to meet these needs, but existing methods relying on supervised classification are often limited to specific areas by the availability of imagery or training data, as well as model transferability. Scaling up and operationalizing these methods for general broadscale monitoring and mapping may be promoted by using simple models that are easily trained and projected across space and time with widely available imagery. Here, we describe a new model that classifies high resolution (~1 m²) 3-band red, green, blue (RGB) imagery from a single point in time into one of four color classes corresponding to tree crown condition or health: green healthy crowns, red damaged or dying crowns, gray damaged or dead crowns, and shadowed crowns where the condition status is unknown. These Tree Crown Health (TCH) models trained on data from the United States (US) Department of Agriculture, National Agriculture Imagery Program (NAIP), for all 48 States in the contiguous US and spanning years 2012 to 2019, exhibited high measures of model performance and transferability when evaluated using randomly withheld testing data (n= 122 NAIP state x year combinations; median overall accuracy 0.89–0.90; median Kappa 0.85–0.86). We present examples of how TCH models can detect and map individual tree mortality resulting from a variety of nationally significant native and invasive forest insects and diseases in the US. We conclude with discussion of opportunities and challenges for extending and implementing TCH models in support of broadscale monitoring and mapping of forest health.