Predicting Future Diameter Distributions Given Current Stand Attributes

This study discussed four methods to project a diameter distribution from age A1 to age A2. Method 1 recovers parameters of the distribution at age A2 from stand attributes at that age. Method 2 uses a stand-level model to grow the quadratic mean diameter, and then recovers the distribution parameters from that prediction. Method 3 grows the diameter distribution by assuming tree-level survival and diameter growth functions. Method 4 first converts the diameter distribution at age A1 into a list of individual trees before growing these trees to age A2. In a numerical example employing the Weibull distribution, methods 3 and 4 produced better results based on two types of error indices and the relative predictive error for each diameter class. Method 4 is a novel method that converts a diameter distribution into a list of individual-trees, and in the process, successfully links together diameter distribution, individual-tree, and whole stand models.

Diameter distribution model development of tropical hybrid Eucalyptus clonal plantations in Sumatera, Indonesia: A comparison of estimation methods

Background: Effective forest management and planning often requires information about the distribution of volume by size and product classes. Size-class models describe the diameter distribution and provide information by diameter class, such as the number of trees, basal area, and volume per unit of area. A successful diameter-distribution model requires high flexibility yet robust prediction of its parameters. To our knowledge, there are no studies regarding diameter distribution models for Eucalyptus hybrids in Indonesia. Therefore, the aim of this study was to compare different recovery methods for predicting parameters of the 3-parameter Weibull distribution for characterising diameter distributions of Eucalyptus hybrid clone plantations, on Sumatera Island of Indonesia. Methods: The parameter recovery approach was proposed to be compatible with stand-average growth and yield models developed based on the same data. Three approaches where compared: moment-based recovery, percentile-based prediction and hybrid methods. The ultimate goal was to recover Weibull parameters from future stand attributes, which were predicted from current stand attributes using regression models. Results: In this study, the moment method was found to give the overall lowest mean error-index and Kolmogorov– Smirnov (KS) statistic, followed by the hybrid and percentile methods. The moment-based method better fit long tails on both sides of the distribution and exhibited slightly greater flexibility in describing plots with larger variance than the other methods. Conclusions: The Weibull approach appeared relatively robust in determining diameter distributions of Eucalyptus hybrid clone plantation in Indonesia, yet some refinements may be necessary to characterize more complex distributions.

Plot size effects on airborne LiDAR-derived metrics and predicted model performance of subtropical planted forest stand attributes

Background Field plot measurement is an essential task for forest inventory and monitoring and ecological applications based on airborne LiDAR. To optimize the field plot size and reduce cost, it is necessary to investigate the influence of field plot size on LiDAR-derived metrics and the accuracy of forest parameter estimation models. Methods A subtropical planted forest with an area of 4,770 ha was used as the study site, and 104 square plot of 900 m2 (30 m×30 m, subdivided into nine quadrats, each with an area of 100 m2 (10 m×10 m)) was divided into field plots with six different areas (100 m2, 200 m2, 300 m2, 400 m2, 600 m2 and 900 m2) by grouping quadrats. The differences in the LiDAR-derived metrics and stand attributes of different sized plots with four forest types (Chinese fir, pine, eucalyptus and broadleaf) were investigated. Through multivariate power models with stable structures, the differences in forest parameter (BA, VOL) estimation accuracies for plots with different sizes were compared. Results (1) The mean differences in LiDAR-derived metrics related to height, density and vertical structure between the plots with different sizes and the 900 m2 plot containing all forest types were very small, and when the plot size changed, these differences changed irregularly; however, the standard deviations of the differences increased rapidly with decreasing plot size. (2) There were significant differences in the mean of the maximal height of the point cloud (Hmax), density of the 75th percentile of the point cloud (dh75) and mean leaf area density (LADmean) (except for Chinese fir and eucalyptus) between the plots with different sizes and the 900 m2 plot containing all forest types; other LiDAR-derived metrics had significant differences in only some or a certain size of plots, but there was no regularity. (3) Except for the maximal tree height of the plot (Hm), the forest stand attributes, including the mean tree height (H), diameter at breast height (DBH), basal area (BA), and stand volume (VOL), of all forest types showed either no significant differences or minimal differences between plots with different sizes and the 900 m2 plot. (4) With increasing plot size, the coefficient of determination (R2) of the estimation models for VOL and BA of all forest types increased gradually, while the relative root mean square error (rRMSE) and mean prediction error (MPE) decreased gradually, and the estimation accuracy of the models improved. Conclusion Due to the heterogeneity of the vertical and horizontal forest structures, some LiDAR-derived metrics and stand parameters for field plots with different sizes varied. As the plot size increased, the variations in the independent variables (LiDAR-derived metrics) and dependent variables (stand parameters) of the estimation models decreased gradually. These changes improved the robustness and accuracy of the models. In the application of airborne LiDAR in forest inventory and monitoring, both prediction accuracy and cost should be considered. For subtropical planted forests, we preliminarily suggest the following appropriate sizes for field plots: 900 m2 for Chinese fir and pine forests, 400 m2 for eucalyptus forests and 600 m2 for broadleaf forests. However, this protocol still needs to be tested in further studies.

Effect of spacing on volume, form factor and taper for Pinus taeda trees in Paraná, Brazil

Several forestry procedures affect tree volume and shape, such as spacing, pruning, and thinning. Studying and understanding the effect of these operations on stand attributes are very important for forest management. This study aimed to evaluate volume, form factor, and taper for Pinus taeda trees stratified into diameter classes within two planting spacings. In addition, we aimed to evaluate the time spent to scale each tree, measured with a chronometer. Indirect scaling was performed using a Criterion RD 1000. Thirty trees were scaled on each planting spacing (3 m x 2 m and 4 m x 2 m), totaling 60 trees encompassing all diameter classes. Tree volume was calculated using the Smalian equation. Tree volume, form factor, and taper were calculated to each tree and evaluated by stand (independent t-test) and diameter class (variance analysis and Tukey test). The average scaling time was 4 minutes and 35 seconds, which decreased with practice (-24%). Form factor and taper differed with spacing and diameter class. Volume did not differ with spacing, but it did in the diameter classes. We concluded that indirect scaling is a practical method for tree volume assessment; higher planting density leads to more cylindrical stems with lower taper ratios in comparison with denser stands; and the fact that tree volume, form factor and taper differed among the diameter classes should be incorporated into studies of taper modeling.

Assessing Forest Structural and Topographic Effects on Habitat Productivity for the Endangered Apennine Brown Bear

Any forest management potentially affects the availability and quality of resources for forest-dwelling wildlife populations, including endangered species. One such species is the Apennine brown bear, a small and unique population living in the central Apennines of Italy. The conservation of this relict bear population is hampered by the lack of knowledge of the fine-scale relationships between productivity of key foods and forest structure, as this prevents the design and implementation of effective forest management plans. To address this issue, we sampled the main structural stand attributes within the bear’s range and used multivariate generalized linear mixed models in a Bayesian framework to relate forest structural attributes to proxies of productivity of key bear foods. We found that hard mast was positively associated with both forest typology and high forest system, but negatively related to both the time elapsed since the last forest utilization and the amount of deadwood. The availability of soft-mast producing species was positively related to past forestry practices but negatively associated with steep slopes historically managed with high tree densities and a low silvicultural disturbance. Our findings also suggest that herb cover was negatively affected by terrain steepness and basal area, while herb productivity was positively affected by northern and southern exposure. Additionally, richness of forest ants was associated with forests characterized by low volume and high density. Our findings confirm that the productivity of natural bear foods is strongly affected by forest structural and topographical characteristics and are relevant as preliminary information for forest management practices to support the long-term conservation of Apennine bears.

Effects of errors in basal area and mean diameter on the optimality of forest management prescriptions

• Key message Errors in forest stand attributes can lead to sub-optimal management prescriptions concerning the set management objectives. When the objective is net present value, errors in mean diameter result in greater losses than similar errors in basal area, and underestimation greater losses than overestimation. • Context Errors in forest inventory data can cause inoptimality losses in the objectives set to forest management. Losses occur when the forest is treated with management prescriptions that are optimal for erroneous data but not for correct data. • Aims We evaluate the effect of varying levels of errors in basal area and mean diameter on the inoptimality losses. • Methods Errors from 20% of overestimation to 20% of underestimation were simulated in basal area and mean diameter. For each stand, the management prescription that maximized the net present value was selected with and without errors. The inoptimality losses were calculated for different error levels. • Results The tested error levels resulted in inoptimality losses of 0.11–3.01%. Errors in mean diameter increased inoptimality losses more than similar relative errors in basal area. Simultaneous underestimation of basal area and mean diameter led to greater inoptimality losses than simultaneous overestimation of these attributes. • Conclusion If the forest is considered as an investment, using inventory data where basal area and mean diameter are underestimated causes greater losses compared with data where these attributes are overestimated. Errors in mean diameter are more important than similar errors in the basal area. Large errors in basal area and mean diameter should be avoided especially in stands where the basal area is high.

ARTIFICIAL NEURAL NETWORKS (ANN) FOR HEIGHT ESTIMATION IN A MIXED-SPECIES PLANTATION OF Eucalyptus globulus LABILL AND Acacia mearnsii DE WILD

ABSTRACT The objective of this study is to evaluate the fit of Artificial Neural Networks (ANN) for height estimation and evaluation of the effects of consortium in a mixed-species plantation of Eucalyptus globulus (E) and Acacia mearnsii (A). The experiment was installed in 2005, on two farms in the municipality of Piratini - RS, where was planted the species Eucalyptus globulus (E) and Acacia mearnsii (A), in monoculture and mixed in simple lines (50%E:50%A - SL), and double lines (50%E:50%A - DL). The training and evaluation of the networks were made in R-project with the package neuralnet. All ANNs, from the simplest to the most complex, showed high values for Rŷy and low for Syx, BIAS and RMSE, with superior results in ANN 3, 4, and 6, which demonstrates that the information of DBHmin, DBHmean, and DBHmax were important stand attributes. Furthermore, the ANNs were able to capture the different growth patterns shown by the species in the different forms of consortiums, therefore is indicated for the height estimation in monocultures and mixed plantations of Eucalyptus globulus and Acacia mearnsii, and only one ANN would be necessary to represent the entire population.

Obtaining tree stand attributes from unmanned aerial vehicle (UAV) data: the case of mixed forests

Nowadays, due to the rapid development of lightweight unmanned aerial vehicles (UAV), remote sensing systems of ultra-high resolution have become available to many researchers. Conventional ground-based measurements for assessing tree stand attributes can be expensive, as well as time- and labor-consuming. Here, we assess whether remote sensing measurements with lightweight UAV can be more effective in comparison to ground survey methods in the case of temperate mixed forests. The study was carried out at the Prioksko-Terrasny Biosphere Nature Reserve (Moscow region, Russia). This area belongs to a coniferous-broad-leaved forest zone. Our field works were carried out on the permanent sampling plot of 1 ha (100×100 m) established in 2016. The coordinates of the plot center are N 54.88876°, E 37.56273° in the WGS 84 datum. All trees with DBH (diameter at breast height) of at least 6 cm (779 trees) were mapped and measured during the ground survey in 2016 (See Fig. 1 and Table 1). Mapping was performed with Laser Technology TruPulse 360B angle and a distance meter. First, polar coordinates of each tree trunk were measured, and then, after conversion to the cartesian coordinates, the scheme of the stand was validated onsite. Species and DBH were determined for each tree. For each living tree, we detected a social status class (according to Kraft). Also for living trees, we measured the tree height and the radii of the crown horizontal projection in four cardinal directions. A lightweight UAV Phantom 4 (DJI-Innovations, Shenzhen, China) equipped with an integrated camera of 12Mp sensor was used for aerial photography in this study. Technical parameters of the camera are available in Table 2. The aerial photography was conducted on October 12, 2017, from an altitude of 68 m. The commonly used mosaic flight mode was used with 90% overlapping both for side and front directions. We applied Agisoft Metashape software for orthophoto mosaic image and dense point cloud building. The canopy height model (CHM) was generated with lidR package in R. We used lasground() function and cloth simulation filter for classification of ground points. To create a normalized dataset with the ground at 0, we used spatial interpolation algorithm tin based on a Delaunay triangulation, which performs a linear interpolation within each triangle, implemented in the lasnormilise() function. CHM was generated according to the pit-free algorithm based on the computation of a set of classical triangulations at different heights. The location and height of individual trees were automatically detected by the function FindTreesCHM() from the package rLIDAR in R. The algorithm implemented in this function is local maximum with fixed window size. Accuracy assessment of automatically detected trees (in QGIS software) was performed through visual interpretation of orthophoto mosaic and comparison with ground survey data. The number of correctly detected trees, omitted by the algorithm and not existing but detected trees were counted. As a result of aerial photography, 501 images were obtained. During these data processing with the Metashape, dense point cloud of 163.7 points / m2 was generated. CHM with 0.5 m resolution was calculated. According to the individual-tree detection algorithm, 241 trees were found automatically (See Fig. 2A). The total accuracy of individual tree detection was 73.9%. Coniferous trees (Pinus sylvestris and Picea abies) were successfully detected (86.0% and 100%, respectively), while results for birch (Betula spp.) required additional treatment. The algorithm correctly detected only 58.2% of birch trees due to false-positive trees (See Fig. 2B and Table 3). These results confirm the published literature data obtained for managed tree stands. Tree heights retrieved from the UAV were well-matched to ground-based method results. The mean tree heights retrieved from the UAV and ground surveys were 25.0±4.8 m (min 8.2 m, max 32.9 m) and 25.3±5.2 m (min 5.9 m, max 34.0 m), respectively (no significant difference, p-value=0.049). Linear regression confirmed a strong relationship between the estimated and measured heights (y=k*x, R2 =0.99, k=0.98) (See Fig. 3A). Slightly larger differences in heights estimated by the two methods were found for birch and pine; for spruce, the differences were smaller (See Fig. 3B and Table 4). We believe that ground measurements of birch and pine height are less accurate than for spruce due to different crown shapes of these trees. So, our results suggested that UAV data can be used for tree stand attributes estimation, but automatically obtained data require validation.