The increasing availability of highly detailed three-dimensional remotely-sensed data depicting forests, including airborne laser scanning (ALS) and digital aerial photogrammetric (DAP) approaches, provides a means for improving stand dynamics information. The availability of data from ALS and DAP has stimulated attempts to link these datasets with conventional forestry growth and yield models. In this study, we demonstrated an approach whereby two three-dimensional point cloud datasets (one from ALS and one from DAP), acquired over the same forest stands, at two points in time (circa 2008 and 2015), were used to derive forest inventory information. The area-based approach (ABA) was used to predict top height (H), basal area (BA), total volume (V), and stem density (N) for Time 1 and Time 2 (T1, T2). We assigned individual yield curves to 20 × 20 m grid cells for two scenarios. The first scenario used T1 estimates only (approach 1, single date), while the second scenario combined T1 and T2 estimates (approach 2, multi-date). Yield curves were matched by comparing the predicted cell-level attributes with a yield curve template database generated using an existing growth simulator. Results indicated that the yield curves using the multi-date data of approach 2 were matched with slightly higher accuracy; however, projections derived using approach 1 and 2 were not significantly different. The accuracy of curve matching was dependent on the ABA prediction error. The relative root mean squared error of curve matching in approach 2 for H, BA, V, and N, was 18.4, 11.5, 25.6, and 27.53% for observed (plot) data, and 13.2, 44.6, 50.4 and 112.3% for predicted data, respectively. The approach presented in this study provides additional detail on sub-stand level growth projections that enhances the information available to inform long-term, sustainable forest planning and management.

Stem size distribution (SSD), which describes tree frequencies in diameter classes within an area, has a variety of direct and indirect applications that are critical for forest management. In this study, we evaluated which structural characteristics derived from Airborne Laser Scanning (ALS) data were best able to differentiate between unimodal and bimodal stands in a managed boreal mixedwood forest in Alberta, Canada. We then used wall-to-wall ALS data to predict (for 20 m-by-20 m grid cells) the parameters of a Weibull SSD in unimodal cells, and a Finite Mixture Model (FMM) in bimodal cells. The resulting SSDs were evaluated for their fit to ground plot-measured SSDs using an Error Index (EI). We found that the variance of ALS return heights was the best metric for differentiating between unimodal and bimodal stands, with a classification accuracy of 77%. Parameters of both the Weibull and FMM distributions were accurately predicted (r² ~ 0.5, Root Mean Square Error (RMSE) ~ 30%), and that differentiating for modality prior to estimating SSD improved the accuracy of estimates (EI of 49.13 with differentiation versus 51.31 without differentiation). Unique to our presented approach is the stratification by SSD modality prior to the modelling of distributions. To achieve this, we apply a threshold to an ALS metric that allows SSD modality to be distinguished for each cell at the landscape level, and this a priori information is then used to ensure that the appropriate distribution is modelled. Our approach is parsimonious and efficient, enabling improved accuracy in SSD estimation across diverse landscapes when ALS data is the sole data source.

The development of red pine (Pinusresinosa Ait.) stands originating under different initial spacings was examined using three measures of growth efficiency that are similar in concept to relative growth rate: diameter at breast height (DBH) increment/crown width, DBH increment/crown projection, and DBH increment/foliage biomass. These three measures of efficiency decreased with increasing DBH in the absence of severe competition and increased with increasing DBH under severe competitive stress. This indicates that small trees occupied their aerial growing space better and utilized their foliage to produce stemwood more efficiently than large trees in the absence of competition, and that the effect of competition was to reverse this trend. Absolute and relative growth rates for DBH and the three measures of growth efficiency were related very significantly to absolute (e.g., crown width) and relative (e.g., crown width/crown length) measures of crown development.

In the United States, forestry accreditation is a nongovernmental, voluntary system of self-examination and peer review that assures the quality of postsecondar

A thinning intensity experiment using theEucalyptus grandis×E. urophyllahybrid was conducted at three sites in Bahia State, Brazil. The treatments were a combination of thinning intensities and pruning: 20%, 35%, and 50% basal area removed with no pruning and 35% basal area removed plus pruning (at 27 months). Plots were measured roughly annually from 27 to 165 months. Thinning was implemented on all sites at 58 months and again at 142 months at two of the sites. One of the sites was harvested at 87 months of age. A linear mixed model was applied separately to each installation to test for differences among treatments for mean increment of height, dominant height, quadratic mean diameter, and volume outside bark at the plot level. Additionally, differences in mean monthly increment of basal area and volume outside bark as a percentage of the value at the beginning of the increment period were examined. Increased thinning intensity increased all tree-level variables except dominant height. Pruning had no impact. Observed mortality on all plots was quite low. Thinning intensity response varied among sites and with time since thinning; however, the thinning intensity response was consistent through time among the installations.

Open global forest cover data can be a critical component for Reducing Emissions from Deforestation and Forest Degradation (REDD+) policies. In this work, we determine the best threshold, compatible with the official Brazilian dataset, for establishing a forest mask cover within the Amazon basin for the year 2000 using the Tree Canopy Cover 2000 GFC product. We compared forest cover maps produced using several thresholds (10%, 30%, 50%, 80%, 85%, 90%, and 95%) with a forest cover map for the same year from the Brazilian Amazon Deforestation Monitoring Project (PRODES) data, produced by the National Institute for Space Research (INPE). We also compared the forest cover classifications indicated by each of these maps to 2550 independently assessed Landsat pixels for the year 2000, providing an accuracy assessment for each of these map products. We found that thresholds of 80% and 85% best matched with the PRODES data. Consequently, we recommend using an 80% threshold for the Tree Canopy Cover 2000 data for assessing forest cover in the Amazon basin.