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Airborne laser scanning (ALS) datasets are used widely for estimating forest biometrics. The transferability of predictive models among ALS acquisitions is a topic of research due to differences in timing, flight parameters, equipment specifications, environmental conditions, and processing methods. The transferability of predictive models therefore is subject to uncertainty. This paper presents an evaluation of the transferability of models for the estimation of stem volume and diameter at breast height (DBH) based on individual tree crown size and competitive neighbourhood metrics derived for managed loblolly pine (Pinus taeda) and slash pine (Pinus elliottii) forest in the Southern USA. Two predictive models types were tested: multiple linear regression (MLR) and Rand Forest (RF). We also evaluated the inclusion of additional training data to model development. Models were able to be transferred to other locations with similar structural and management conditions as the original training dataset with little decrease in accuracy, specifically unthinned stands, despite different ALS acquisitions (Plot stem volume: R2 0.7–0.8; NRMSE 10–12%; mean DBH: R2 0.4–0.7; NRMSE 10–17%; plot basal area: R2 0.7–0.8; NRMSE 12%). Increases in structural differences between the training and test data, driven by age or thinning status, introduced unacceptable levels of uncertainty (Stem volume: R2 0.4–0.7; NRMSE 12–16%; mean DBH: R2 0.4–0.5; NRMSE 18–20%; plot basal area: R2 0.5–0.6; NRMSE 22–40%). Generally, RF models most accuracy estimated DBH, and MLR for stem volume. Improvements to estimate accuracy can be achieved through the addition of relatively small datasets, representing features which were not present in the original data. ALS's ability to provide accurate and near-complete inventories of forests hold a great deal of potential for forest management. The existence of a transferable model that can be used across different acquisitions represents a saving in terms of cost and time, we would argue that future research is therefore warranted. Novel LiDAR-based allometric models were tested using new LiDAR acquisitions; LiDAR predictions were based on tree crown size and immediate neighbourhood metrics; Model inputs were derived from drone and crewed aircraft LiDAR acquisitions; Model predictions were most accurate when applied to similar stand structures as the original model; Normalized root means square error for stem volume and diameter in pine stands with no thinning was <15%; Stem size estimates in thinned stands were consistently overestimated. [ABSTRACT FROM AUTHOR]

Airborne discrete-return light detection and ranging (lidar) can be used to estimate leaf area index (LAI) with relatively high accuracy. This capacity was explored with regard to assessing the capability of estimating LAI at different heights at the plot level, in the presence of understorey vegetation, within intensively managed Loblolly pine forest in North Carolina, USA. Field measurements utilized the LI-COR LAI-2200 plant canopy analyser for field-based estimates of effective LAI at three elevations within each plot; these were on the ground (0.0 m) and 1.0 m and 2.5 m above the ground within the various understorey heights and densities. A number of new and previously existing lidar metrics and indices were calculated from the distribution of return heights, which have been identified as potentially strong predictors of LAI. A bivariate and stepwise regression approach was then applied to create models for the estimation of LAI from lidar-derived height distribution metrics. The results show that specific logarithm transformed laser penetration indices calculated using a height threshold (e.g. the number of returns below 2.5 m ratioed against all returns) as close to field LAI measurement height (e.g. 2.5 m) was more effective than other lidar metrics. LAI can be estimated for each of the three measurement heights within the understorey component explaining 67 to 76% of the variance (root mean square error 0.42–0.57). The indices that produced the highest correlations and which were selected in stepwise regression analysis were calculated using all returns. The results indicate that LAI can be estimated accurately using lidar data in pine plantation forest over a variety of stand conditions. [ABSTRACT FROM PUBLISHER]

The tree crown's directionality of growth may be an indicator of how aggressive the tree is in terms of foraging for light. Airborne drone laser scanning (DLS) has been used to accurately classify individual tree crowns (ITCs) and derive size metrics related to the crown. We compare ITCs among 6 genotypes exhibiting different crown architectures in managed loblolly pine ( Pinus taeda L. ) in the United States. DLS data are classified into ITC objects, and we present novel methods to calculate ITC shape metrics. Tree stems are located using (a) model-based clustering and (b) weighting cluster-based size. We generated ITC shape metrics using 3-dimensional (3D) alphashapes in 2 DLS acquisitions of the same location, 4 years apart. Crown horizontal distance from the stem was estimated at multiple heights, in addition to calculating 3D volume in specific azimuths. Crown morphologies varied significantly ( P < 0.05) spatially, temporally, and among the 6 genotypes. Most genotypes exhibited larger crown volumes facing south (150° to 173°). We found that crown asymmetries were consistent with (a) the direction of solar radiation, (b) the spatial arrangement and proximity of the neighboring crowns, and (c) genotype. Larger crowns were consistent with larger increases in stem volume, but that increases in the southern portions of crown volume were consistent with larger stem volume increases, than in the north. This finding suggests that row orientation could influence stem growth rates in plantations, particularly impacting earlier development. These differences can potentially reduce over time, especially if stands are not thinned in a timely manner once canopy growing space has diminished., Competing Interests: Competing interests: The authors declare that they have no competing interests., (Copyright © 2024 Matthew J. Sumnall et al.)