Crown Base Height Research Articles

Cultivar and tree size, but not climate, are principal factors affecting stem quality of Populus tomentosa plantations in the North China Plain

In order to explore the management strategies for cultivating and improving the stem quality of Populus tomentosa plantations under the background of climate change, this study focuses on P. tomentosa plantations over 10 years old in the North China Plain. Using linear mixed models and ordered logistic models, the impacts of cultivar, tree size, stand age, competition, and climate on the stem quality of P. tomentosa (including crown base height, tapering, branching grade, and straightness grade) were analyzed. The study found that: cultivar significantly affected all stem quality indicators (P < 0.05). Compared to other cultivars, the P. tomentosa f. yixianensis had a 23 % increase in branch height, an 8 % reduction in taper, and the risk of having poorer branches and stem form decreased by 96 % and 80 %, respectively. In addition, taller and bigger-diameter trees had better external stem quality. The impacts of competition-related indicators on stem quality were inconsistent: reduced canopy openness could improve stem quality by enhancing light competition, however, increased tree density increased the risk of deteriorating branching and straightness grade by 1.2 % and 0.9 %, respectively. Among all factors, cultivar and individual tree size had the greatest relative importance for various stem quality indicators, followed by competition-related factors, while stand age and climate factors have no significant impact on P. tomentosa stem quality (P > 0.05). Currently, climate change has little impact on the external stem quality characteristics during the cultivation of P. tomentosa plantations. Management strategies for stem quality can focus on cultivar selection and competition regulation. It is worth noting that increasing tree density in the North China Plain may not necessarily improve stem quality of P. tomentosa plantations, so caution is needed in the process of regulating competition.

ALS-Based, Automated, Single-Tree 3D Reconstruction and Parameter Extraction Modeling

The 3D reconstruction of point cloud trees and the acquisition of stand factors are key to supporting forestry regulation and urban planning. However, the two are usually independent modules in existing studies. In this work, we extended the AdTree method for 3D modeling of trees by adding a quantitative analysis capability to acquire stand factors. We used unmanned aircraft LiDAR (ALS) data as the raw data for this study. After denoising the data and segmenting the single trees, we obtained the single-tree samples needed for this study and produced our own single-tree sample dataset. The scanned tree point cloud was reconstructed in three dimensions in terms of geometry and topology, and important stand parameters in forestry were extracted. This improvement in the quantification of model parameters significantly improves the utility of the original point cloud tree reconstruction algorithm and increases its ability for quantitative analysis. The tree parameters obtained by this improved model were validated on 82 camphor pine trees sampled from the Northeast Forestry University forest. In a controlled experiment with the same field-measured parameters, the root mean square errors (RMSEs) and coefficients of determination (R2s) for diameters at breast height (DBHs) and crown widths (CWs) were 4.1 cm and 0.63, and 0.61 m and 0.74, and the RMSEs and coefficients of determination (R2s) for heights at tree height (THs) and crown base heights (CBHs) were 0.55 m and 0.85, and 1.02 m and 0.88, respectively. The overall effect of the canopy volume extracted based on the alpha shape is closest to the original point cloud and best estimated when alpha = 0.3.

LadderFuelsR: A new automated tool for vertical fuel continuity analysis and crown base height detection using light detection and ranging

Abstract Widespread fuel accumulation in various strata is increasing the risk of high‐severity, crown‐fires. Knowing the vertical forest structure is a crucial factor for fire management planning. Light detection and ranging (LiDAR) sensors have successfully retrieved the main characteristics of the vertical structure of forests. However, detecting and vertically mapping the various fuel strata that can act as ladder fuels (i.e. fuel arrangements that will bring the fire from the surface to the canopy) is challenging and limits our ability to manage the most hazardous forests. Here we show how using LiDAR data and the LadderFuelsR package, developed in the R platform, can provide an automated tool for analysing the vertical fuel structure of a forest and to calculate crown base height (CBH) at tree‐level, among other parameters. We include a suite of tools integrated into a sequential workflow for: (1) calculating the leaf area density (LAD) profiles of each segmented tree; (2) identifying gaps and the non‐continuous fuel layers present; (3) estimating the vertical distance between fuel layers; and (4) retrieving their base height and depth. Additionally, other functions recalculate previous metrics after considering vertical distances greater than certain threshold and calculates the LAD percentage comprised in each fuel layer removing fuel layers below a specified value. Moreover, it calculates tree's CBH based on three criteria: maximum LAD, and both the largest and the last vertical distance between fuel layers. Additionally, when the LAD profiles showed only one fuel layer with CBH at the minimum base height, it identifies the tree's CBH by performing a segmented linear regression. Finally, a collection of plotting functions is developed to represent all previous metrics. This tool has been tested with different LiDAR sensors and thousands of trees in several Pinus pinaster forest areas in Central Spain, and its accuracy has been evaluated using field‐based tree CBH in Pinus palustris Mills dominated forests in Florida (USA). This tool provides accurate information about the vertical structure of a forest, which can be used for prioritizing treatment areas to reduce fire hazard or identify high crown‐fire risk areas.

A Novel Method for Extracting DBH and Crown Base Height in Forests Using Small Motion Clips

The diameter at breast height (DBH) and crown base height (CBH) are important indicators in forest surveys. To enhance the accuracy and convenience of DBH and CBH extraction for standing trees, a method based on understory small motion clips (a series of images captured with slight viewpoint changes) has been proposed. Histogram equalization and quadtree uniformization algorithms are employed to extract image features, improving the consistency of feature extraction. Additionally, the accuracy of depth map construction and point cloud reconstruction is improved by minimizing the variance cost function. Six 20 m × 20 m square sample plots were selected to verify the effectiveness of the method. Depth maps and point clouds of the sample plots were reconstructed from small motion clips, and the DBH and CBH of standing trees were extracted using a pinhole imaging model. The results indicated that the root mean square error (RMSE) for DBH extraction ranged from 0.60 cm to 1.18 cm, with relative errors ranging from 1.81% to 5.42%. Similarly, the RMSE for CBH extraction ranged from 0.08 m to 0.21 m, with relative errors ranging from 1.97% to 5.58%. These results meet the accuracy standards required for forest surveys. The proposed method enhances the efficiency of extracting tree structural parameters in close-range photogrammetry (CRP) for forestry. A rapid and accurate method for DBH and CBH extraction is provided by this method, laying the foundation for subsequent forest resource management and monitoring.

Testing treecbh in Central European forests: an R package for crown base height detection using high-resolution aerial laser-scanned data

Abstract Accurate information regarding tree canopy characteristics is crucial for forest management, but it is often difficult to assess. This study presents an innovative framework designed for crown base height (CBH) detection using high-resolution laser-scanned data, with a specific focus on individual trees within forests. The framework comprises three key steps: (i) segmenting the input tree point cloud to identify the tree trunk and its branches using the treesio software; (ii) applying vertical cross-sectional K-means clustering to cluster the identified tree and to define the elevation threshold for removing low-lying understory vegetation; (iii) employing a novel 2D kernel method for detecting CBH after eliminating low-lying understory vegetation. The 2D kernel method, developed for broadleaf forests using leaf-off airborne laser scanning (ALS) data, underpins the treecbh tool. This tool features a visual CBH adjustment component that shows a 2D profile plot of the tree point cloud, and suggests a CBH value for user approval or adjustment. To evaluate accuracy, in situ measured CBH data from five forest plots in Germany and Hungary with varied species compositions were used. ALS data were collected during leaf-off conditions for the two Hungarian plots and during leaf-on conditions for the three German plots. Leaf-off terrestrial laser-scanned data from individual trees were also used in the accuracy assessment. A sensitivity analysis using random point decimation was conducted on the terrestrial laser-scanned data to assess treecbh’s sensitivity to point density. The initial results exhibited matching rates of 45% and 60% for leaf-off ALS plots, which significantly improved to 71% and 77%, respectively, when using the visual CBH adjustment feature of the tool. The leaf-on ALS results demonstrated matching rates between 24% and 33%, whereas the CBHs of individual terrestrial laser-scanned trees could be detected with 93% accuracy in visual mode. It was observed that treecbh operates effectively when the input ALS data have a minimum point density of 20 pts/${\text{m}}^2$, with its optimal performance achieved at 110 pts/${\text{m}}^2$. These findings indicated treecbh’s sensitivity to ALS data quality, scanning season (leaf-on and leaf-off), and point density. This sensitivity can be effectively mitigated in the case of leaf-off ALS data by utilizing the visual CBH adjustment feature of the tool.

Nitrogen and phosphorus additions alter soil N transformations in a Metasequoia glyptostroboides plantation.

Nitrogen (N) and phosphorus (P) enrichment due to anthropogenic activities can significantly affect soil N transformations in forest ecosystems. However, the effects of N and P additions on nitrification and denitrification processes in Metasequoia glyptostroboides plantations, and economically important forest type in China, remain poorly understood. This study investigated the responses of soil nitrification and denitrification rates, as well as the abundances of nitrifiers and denitrifiers, to different levels of N and P additions in a 6-year nutrient addition experiment in a M. glyptostroboides plantation. Stepwise multiple regression analysis was used to identify the main predictors of nitrification and denitrification rates. The results showed that moderate N addition (N2 treatment, 2.4 mol·m-2) stimulated nitrification rates and abundances of ammonia-oxidizing archaea (AOA) and bacteria (AOB), while excessive N and P additions inhibited denitrification rates and reduced the abundance of nirS-type denitrifiers. AOB abundance was the main predictor of nitrification rates under N additions, whereas microbial biomass carbon and nirS gene abundance were the key factors controlling denitrification rates. Under P additions, tree growth parameters (diameter at breast height and crown base height) and AOB abundance were the primary predictors of nitrification and denitrification rates. Our study reveals complex interactions among nutrient inputs, plant growth, soil properties, and microbial communities in regulating soil N transformations in plantation forests. This study also offers valuable insights for formulating effective nutrient management strategies to enhance the growth and health of M. glyptostroboides plantations under scenarios of increasing elevated nutrient deposition.

Revealing Three-Dimensional Variations in Fuel Structures in Subtropical Forests through Backpack Laser Scanning

Wildfire hazard is a prominent issue in subtropical forests as climate change and extreme drought events increase in frequency. Stand-level fuel load and forest structure are determinants of forest fire occurrence and spread. However, current fuel management often lacks detailed vertical fuel distribution, limiting accurate fire risk assessment and effective fuel policy implementation. In this study, backpack laser scanning (BLS) is used to estimate several 3D structural parameters, including canopy height, crown base height, canopy volume, stand density, vegetation area index (VAI), and vegetation coverage, to characterize the fuel structure characteristics and vertical density distribution variation in different stands of subtropical forests in China. Through standard measurement using BLS point cloud data, we found that canopy height, crown base height, stand density, and VAI in the lower and middle-height strata differed significantly among stand types. Compared to vegetation coverage, the LiDAR-derived VAI can better show significant stratified changes in fuel density in the vertical direction among stand types. Among stand types, conifer-broadleaf mixed forest and C. lanceolata had a higher VAI in surface strata than other stand types, while P. massoniana and conifer-broadleaf mixed forests were particularly unique in having a higher VAI in the lower and middle-height strata, corresponding to the higher surface fuel and ladder fuel in the stand, respectively. To provide more informative support for forest fuel management, BLS LiDAR data combined with other remote sensing data were advocated to facilitate the visualization of fuel density distribution and the development of fire risk assessment.

Individual tree segmentation in occluded complex forest stands through ellipsoid directional searching and point compensation

Terrestrial laser scanning (TLS) accurately captures tree structural information and provides prerequisites for tree-scale estimations of forest biophysical attributes. Quantifying tree-scale attributes from TLS point clouds requires segmentation, yet the occlusion effects severely affect the accuracy of automated individual tree segmentation. In this study, we proposed a novel method using ellipsoid directional searching and point compensation algorithms to alleviate occlusion effects. Firstly, region growing and point compensation algorithms are used to determine the location of tree roots. Secondly, the neighbor points are extracted within an ellipsoid neighborhood to mitigate occlusion effects compared with k-nearest neighbor (KNN). Thirdly, neighbor points are uniformly subsampled by the directional searching algorithm based on the Fibonacci principle in multiple spatial directions to reduce memory consumption. Finally, a graph describing connectivity between a point and its neighbors is constructed, and it is utilized to complete individual tree segmentation based on the shortest path algorithm. The proposed method was evaluated on a public TLS dataset comprising six forest plots with three complexity categories in Evo, Finland, and it reached the highest mean accuracy of 77.5%, higher than previous studies on tree detection. We also extracted and validated the tree structure attributes using manual segmentation reference values. The RMSE, RMSE%, bias, and bias% of tree height, crown base height, crown projection area, crown surface area, and crown volume were used to evaluate the segmentation accuracy, respectively. Overall, the proposed method avoids many inherent limitations of current methods and can accurately map canopy structures in occluded complex forest stands.

Assessing the benefits and management of urban forest in supporting low carbon city in Jakarta, Indonesia

Abstract. Aulia R, Kaswanto, Arifin HS, Mosyaftiani A, Syasita N, Wahyu A, Wiyoga H. 2023. Assessing the benefits and management of urban forest in supporting low carbon city in Jakarta, Indonesia. Biodiversitas 24: 6151-6159. Climate change is a phenomenon that has become a global concern including in urban areas. Jakarta is one of the most populated cities in Indonesia, reaching 10.56 million people in 2020 with high emissions levels due to high human activities. Thus, efforts to reduce carbon emissions to mitigate climate change are necessary in Jakarta one of which is through the concept of low carbon city and manifested in the establishment of Green Open Space (GOS), including urban forests. Urban forests aim to sequester and store carbon, and improve the urban microclimate. This research aimed to assess the benefits of urban forests in Jakarta in term of their capacity in carbon sequestration using i-Tree Eco tool, and analyse the management of urban forests related to the applicable regulations. To collect data on trees, this study created 49 plots scattered across Srengseng Urban Forest, Cipayung Urban Forest, Munjul Urban Forest, Rawa Malang Urban Forest, and Pondok Labu Urban Forest. Tree species, Diameter at Breast Height (DBH), total tree height, live tree height, crown base height, crown width, percent crown loss, and crown light exposure were all gathered. Furthermore, in-depth interviews with urban forest managers were performed to gather additional information on present management, including questions derived from Jakarta's green open space legislation to assess management efficacy. The results of the analysis of urban forest quality show that the health status of trees is one of the factors that indicate the quality of urban forests at the five study sites, with more than 50% of the total trees in fair condition (75- 90% health). The gross carbon sequestration of trees in five urban forests Jakarta is about 184.8 metric tons of carbon per year. Additionally, the in-depth interviews also test the mortality of the urban forest, a decent indicator of current management with estimates over the next 10 years, showing a slight increase in carbon sequestration and storage capacity. However, the mortality scenario leads to a trend toward a decrease in leaf area and the number of tree categories, especially in the Srengseng Urban Forest.

A Framework for Analyzing Individual-Tree and Whole-Stand Growth by Fusing Multilevel Data: Stochastic Differential Equation and Copula Network

In forestry, growth functions form the basis of research and are widely used for the mathematical modeling of stand variables, e.g., tree or stand basal area, stand height, stand volume, site index, and many more. In this study, to estimate five-dimensional dependencies between tree diameter at breast height, potentially available area, height, crown area and crown base height, we used a normal copula approach whereby the growths of individual variables are described using a stochastic differential equation with mixed-effect parameters. The normal copula combines the marginal distributions of tree diameter at breast height, potentially available area, height, crown area, and crown base height into a joint multivariate probability distribution. Copula models have the advantage of being able to use collected longitudinal, multivariate, and discrete data for which the number of measurements of individual variables does not match. This study introduced a normalized multivariate interaction information measure based on differential entropy to assess the causality between tree size variables. In order to accurately and quantitatively assess the stochastic processes of the tree size variables’ growth and to provide a scientific basis for the formalization of models, an analysis method of the synergetic theory of information entropy has been proposed. Theoretical findings are illustrated using an uneven-aged, mixed-species empirical dataset of permanent experimental plots in Lithuania.

A forest gap is not forever: Towards an objective standard to determine when a gap is considered closed in temperate forests

Forest gaps create environmental heterogeneity and drive forest dynamics through gap-phase regeneration. However, it is unclear when a forest gap can be considered closed, limiting a global synthesis of forest succession that integrates gap patterns and dynamics. We created twelve forest gaps (six large gaps, RD/H [ratio of gap diameter to gap border tree height] = 1.3; six small gaps, RD/H = 0.6) and monitored tree regeneration for seventeen years to establish an objective gap-closure standard for regeneration filling in a temperate secondary forest. Our results indicated that a forest gap could be considered closed or entering a gap-closure phase when the dominant gap regeneration layer (i.e., the top 10% gap-fillers) (1) reached the crown base height (8.9 m on average) of canopy trees and (2) entered a period of stability in density, richness, and evenness. Linear mixed-effects models found that these two conditions met each other in the same period (14.5 years) in the large gaps, with a mean density, richness, and evenness of 0.1 (stem m−2), 4.9 (species gap−1), and 0.9, respectively. However, if no repeated gap disturbance occurred, small gaps were closed by lateral extension rather than by height growth. Light-demanding species (e.g., Juglans mandshurica) mainly contributed to the closure of large gaps and played negligible roles in the small gaps, but intermediate or shade-tolerant species (e.g., Acer mono) in the small gaps had potential for successful gap filling following repeated gap disturbance. Our gap-closure standard links gap structure to species composition and provides a reference for large-scale research on gap patterns and dynamics, which helps to understand the ecological process during forest succession. The standard also provides a relatively clear boundary between forest gaps and closed forest stands, which is a premise to develop appropriate forest management and shorten the restoration period of degraded secondary forests.

A Statistical Dependence Framework Based on a Multivariate Normal Copula Function and Stochastic Differential Equations for Multivariate Data in Forestry

Stochastic differential equations and Copula theories are important topics that have many advantages for applications in almost every discipline. Many studies in forestry collect longitudinal, multi-dimensional, and discrete data for which the amount of measurement of individual variables does not match. For example, during sampling experiments, the diameters of all trees, the heights of approximately 10% of the trees, and the tree crown base height and crown width for a significantly smaller number of trees are measured. In this study, for estimating five-dimensional dependencies, we used a normal copula approach, where the dynamics of individual tree variables (diameter, potentially available area, height, crown base height, and crown width) are described by a stochastic differential equation with mixed-effect parameters. The approximate maximum likelihood method was used to obtain parameter estimates of the presented stochastic differential equations, and the normal copula dependence parameters were estimated using the pseudo-maximum likelihood method. This study introduced the normalized multi-dimensional interaction information index based on differential entropy to capture dependencies between state variables. Using conditional copula-type probability density functions, the exact form equations defining the links among the diameter, potentially available area, height, crown base height, and crown width were derived. All results were implemented in the symbolic algebra system MAPLE.

Mapping individual tree and plot-level biomass using airborne and mobile lidar in piñon-juniper woodlands

Piñon-juniper (PJ) woodlands are a widespread dryland ecosystem in the US containing an immense but poorly-constrained amount of aboveground biomass (AGB). Found at the dry end of the climatic range within which trees can persist, PJ faces an uncertain future in a changing climate, giving unique importance to mapping its AGB, past, present, and future. Lidar remote sensing offers great potential towards that end with research in a range of tree-dominant ecosystems demonstrating a strong capacity for mapping AGB. However, studies applying lidar to the task of mapping AGB in PJ are few. Given the unique structural characteristics of PJ trees, which tend to be short in stature (<10 m), possess multiple stems often obscured by low crown base heights, and irregularly-shaped canopies, there remains a high degree of uncertainty surrounding tree- and stand-level structural mapping in these woodlands. To resolve our limited understanding of how to best quantify AGB in PJ using lidar, we conducted a field-validated analysis comparing lidar platform (airborne laser scanner, ALS vs. mobile laser scanner, MLS) and analytical framework (area-based modeling vs. individual tree-based modeling). We used a random forest machine learning approach to predicting tree- and stand-level structure with leave-one-out cross-validation to assess model performance. In an area-based context, MLS only marginally outperformed ALS, with both able to explain approximately 80 % of variance in AGB. However, in an individual tree-based context, MLS significantly outperforms ALS at several tasks, including: (1) delineating tree crowns (MLS delineated 100 % of trees; ALS delineated 69 %); (2) directly characterizing tree structural parameters including height (R2ALS = 0.52 vs. R2MLS = 0.74) and crown area (R2ALS = 0.26 vs. R2MLS = 0.68); and (3) modeling AGB (R2ALS = 0.38 vs. R2MLS = 0.62). We attribute this difference in performance to the vastly greater structural detail contained within the MLS point cloud data as compared to ALS, with the former possessing an average of approximately 30,000 pts/m2 and the latter 14 pts/m2. Based on our results, we make the following recommendation: if individual tree structure is needed, MLS is the far superior option; however, given the wider availability, ALS data are a suitable substitute when mapping PJ AGB, particularly under an area-based modeling analytical framework. This study represents the first time MLS data have been applied to tree structure mapping in PJ, and provides strong quantitative evidence towards best structural and AGB mapping practices in this unique, widespread, and important ecosystem. It serves as a solid foundation upon which future studies aiming to map AGB in PJ and other similar dryland woodland ecosystems can be built.

Pipe Model Can Accurately Estimate Crown Biomass of Larch (Larix olgensis) Plantation Forest in Northeast China

The pipe model theory has been applied to estimate allometry of trees in many regions; however, its reliability and generality need more verification for estimating crown biomass in China. In the present study, the crown biomass of Larix olgensis plantations in four sites in northeast China was estimated using the pipe model, and the correlation efficiency index of larch crown biomass for pipe model estimation was 0.953. The crown biomass of larch plantations could be accurately estimated by the tree height, crown base height, and stem area at breast height. Meanwhile, the effects of site, stand density, and age on the accuracy of crown biomass estimated by the pipe model were detected. The covariance analysis showed that the effect of age on crown biomass was 0.024, indicating that age had a significant effect on the estimation accuracy in this model, while site and stand density had no significant effects (p = 0.180 and p = 0.169). Our study showed that the crown biomass of L. olgensis plantations in northeast China could be accurately estimated using the pipe model, and we recommend considering the age effect in practical applications.

Crown-Level Structure and Fuel Load Characterization from Airborne and Terrestrial Laser Scanning in a Longleaf Pine (Pinus palustris Mill.) Forest Ecosystem

Airborne Laser Scanners (ALS) and Terrestrial Laser Scanners (TLS) are two lidar systems frequently used for remote sensing forested ecosystems. The aim of this study was to compare crown metrics derived from TLS, ALS, and a combination of both for describing the crown structure and fuel attributes of longleaf pine (Pinus palustris Mill.) dominated forest located at Eglin Air Force Base (AFB), Florida, USA. The study landscape was characterized by an ALS and TLS data collection along with field measurements within three large (1963 m2 each) plots in total, each one representing a distinct stand condition at Eglin AFB. Tree-level measurements included bole diameter at breast height (DBH), total height (HT), crown base height (CBH), and crown width (CW). In addition, the crown structure and fuel metrics foliage biomass (FB), stem branches biomass (SB), crown biomass (CB), and crown bulk density (CBD) were calculated using allometric equations. Canopy Height Models (CHM) were created from ALS and TLS point clouds separately and by combining them (ALS + TLS). Individual trees were extracted, and crown-level metrics were computed from the three lidar-derived datasets and used to train random forest (RF) models. The results of the individual tree detection showed successful estimation of tree count from all lidar-derived datasets, with marginal errors ranging from −4 to 3%. For all three lidar-derived datasets, the RF models accurately predicted all tree-level attributes. Overall, we found strong positive correlations between model predictions and observed values (R2 between 0.80 and 0.98), low to moderate errors (RMSE% between 4.56 and 50.99%), and low biases (between 0.03% and −2.86%). The highest R2 using ALS data was achieved predicting CBH (R2 = 0.98), while for TLS and ALS + TLS, the highest R2 was observed predicting HT, CW, and CBD (R2 = 0.94) and HT (R2 = 0.98), respectively. Relative RMSE was lowest for HT using three lidar datasets (ALS = 4.83%, TLS = 7.22%, and ALS + TLS = 4.56%). All models and datasets had similar accuracies in terms of bias (&lt;2.0%), except for CB in ALS (−2.53%) and ALS + TLS (−2.86%), and SB in ALS + TLS data (−2.22%). These results demonstrate the usefulness of all three lidar-related methodologies and lidar modeling overall, along with lidar applicability in the estimation of crown structure and fuel attributes of longleaf pine forest ecosystems. Given that TLS measurements are less practical and more expensive, our comparison suggests that ALS measurements are still reasonable for many applications, and its usefulness is justified. This novel tree-level analysis and its respective results contribute to lidar-based planning of forest structure and fuel management.

Estimating Mediterranean stand fuel characteristics using handheld mobile laser scanning technology

Background Accurate, timely and easily obtainable information on stand fuel is of great importance in the prediction of fire behaviour. Aims The objective of this study is to measure several stand fuel characteristics with handheld mobile laser scanning (HMLS) in six fuel types for Mediterranean region, and compare the results with traditional field fuel measurements (FFM) in 35 different sampling plots. Methods The measurements involved overstorey (the number of trees, diameter at breast height, crown base height, tree height, maximum tree height, stand crown closure) and understorey (understorey closure, understorey height) fuel characteristics, and ground slope. Correlation analysis and t-test were performed to examine the relationship between FFM and HMLS datasets. In addition, cross-validation statistics (RMSE, rRMSE and R2) were employed to evaluate the accuracy of the HMLS method. Key results The results indicated strong correlations among all fuel characteristics. However, overstorey fuel characteristics were more favourable (r-values between 0.804 and 0.996, P &lt; 0.01) than understorey (r-values between 0.483 and 0.612, P &lt; 0.01). There was no significant difference between FFM and HMLS datasets in all fuel characteristics (P &gt; 0.05). Conclusions The results indicated that the HMLS was practical, cost-effective, time-efficient and required less labour as compared to traditional FFM in plot-level (i.e. 0.1 ha) inventories.

Can We Predict Male Strobili Production in Araucaria angustifolia Trees with Dendrometric and Morphometric Attributes?

Knowledge of the formation and correlation of reproductive structures with dendro/morphometric variables of the Araucaria angustifolia tree species is a tool for its conservation and viability for sustainable forest management. We counted visually in araucaria trees the number of male strobili in RGB images acquired by Remotely Piloted Aircraft System (RPAs) over forest remnants. The diameter at the breast height (d), total height (h), crown radii (cr), crown base height (cbh), periodic annual increment in d based on increment rolls were measured, and the morphometric indices and crown efficiency were calculated with these variables. The relationships of these variables with male strobili production were analyzed by Pearson’s correlation and multivariate analysis techniques (cluster, factorial analysis, and main components). The morphometric variables correlated with the production of male strobili were d (r = 0.58, p-0.0002), crown diameter (r = 0.62, p &lt; 0.0001), crown area (r = 0.62, p &lt; 0.0001), coverage index (r = 0.51, p-0.001) and slenderness (r = −0.39, p-0.01). We argue that the production of male strobili is related to the vitality, dimension, density, growth space, and position in the stratum of the tree inside the forest, inferring a relationship between reproductive structures with the shape, size, growth space, and tree density. Such aspects shall be considered in future forest management initiatives in Southern Brazil.

Testing the application of process-based forest growth model PREBAS to uneven-aged forests in Finland

The challenges of applying process-based models to uneven-aged forests are the difficulties in simulating the interactions between trees and resource allocation between size classes. In this study, we focused on a process-based forest growth model PREBAS which is a mean tree model with Reineke self-thinning mortality and was originally developed for even-aged forests. The primary aim was to test the application of PREBAS model to uneven-aged forests by introducing different diameter at breast height (DBH) size classes to better represent the forest structure. Additionally, we introduced a new mortality model MORnew to PREBAS which is developed for uneven-aged stands and compared with the current PREBAS version in which a modification Reineke rule is used. The tests were conducted in 26 old Norway spruce dominated stands in southern and central Finland with three consecutive measurements (on average a 25-year study period). To evaluate the model performance, we compared the estimations of stand averaged diameter at breast height (D), stand averaged tree height (H), stand averaged crown base height (Hc), stand basal area (B) and density (N) with measurements. Moreover, biomass estimations of each tree component (foliage, branch and stem) were compared to estimations from empirical models. Results showed that introducing size distributions can represent better stand structure and improve the model predictions compared with data. Moreover, the new mortality model MORnew showed promise with qualitatively more realistic results especially among the largest tree size classes. However, model bias still existed in the simulation although the predictions were improved. It revealed that further calibration of the PREBAS model with size classes should be done to better extend the model applicability to uneven-aged forests.

Automatic tree crown segmentation using dense forest point clouds from Personal Laser Scanning (PLS)

Among digital-based technologies to monitor forest ecosystems, personal laser scanning (PLS) has high potential to characterize even complex deciduous and rainforests. PLS data include a complete and detailed 3D representation of forest stands, but tree individuals need to be segmented accurately before retrieving tree characteristics. As manual on-screen segmentation is time-consuming and labor intensive, we suggest an automatic voxel-based region growing crown segmentation algorithm. Diameter at breast height (dbh), tree height, crown base height (cbh), crown projection area (cpa) and crown volume were automatically extracted from single tree point clouds. The methodology was validated on previously published PLS raw data in terms of segmentation accuracy and measurement precision. Manual segmentation, field measurements, and geometrical crown models were used as reference data. The overall segmentation accuracy of the crowns was 87.02% and tree height was accurately measured with a bias of −0.05 m and a root mean square deviation (RMSD) of 1.21 m (6.33%). Existing geometric crown models proved to be a realistic approximation of the true crown architecture and matched the measured tree crown volume with a bias of −4.62 m 3 and a RMSD of 63.02 m 3 (31.72%). Tree height and cpa were not affected by segmentation accuracy, but a major challenge remained in estimating cbh. The proposed methodology provides an efficient and low-cost solution for a fully automatic and digital forest inventory. • Personal Laser Scanning is suitable to support forest inventory. • Efficient and accurate measurements on single trees require automatic software. • A region growing algorithm for individual tree segmentation achieved 87% accuracy. • Automatic tree height measurement has the same accuracy as field measurement.

Small-Scale Analysis of Characteristics of the Wildland–Urban Interface Area of Thessaloniki, Northern Greece

In the past few years, the continuous expansion of urban development has created mixed forested, agricultural, and urban areas. These areas are called the wildland–urban interface (WUI), and they are characterized by increased human activities and land-use conversion, and they usually contribute to a high risk of wildfire occurrence. In the case of the peri-urban areas of Thessaloniki city, an effort was made to map, classify, and describe this wildland–urban interface, using Sentinel-2 satellite images of the area and very large scale orthophotos (VLSO) for the human settlements. Object-based image analysis (OBIA) was applied to classify landcover, combined with analysis of field data. The results showed that the WUI area in the city of Thessaloniki appears to the north and east of the city and covers an area of 2203.98 ha. The main characteristics affected by the ecological conditions of this area are the building (or human infrastructure) density, type, and the structure of forest vegetation. Human population pressure was found to be greatly differentiated between WUI areas belonging to different municipalities, the most affected was the municipality of Thessaloniki. A set of fire prevention silvicultural treatments are suggested for mitigating the fire danger in the area, accompanied by appropriate human awareness actions and the involvement of the local society. These measures include the reduction in crown bulk density and increase in crown base height through pruning (at least to 1/3 of total tree height), and low thinning, aiming to ensure that tree crowns of mature Pinus brutia trees are not in contact with one another. Both in the young P. brutia forest and the evergreen vegetation areas, thinning, pruning, and vegetation clearing is recommended adjusted according to each ecosystem.