Search Results (979)

Hollow-dependent wildlife has been declining globally due to the removal of hollow-bearing trees, yet these trees are often unaccounted for in habitat mapping. As on-ground field surveys are costly and time-consuming, we aimed to develop a simple, accessible and transferrable geospatial approach using freely accessible LiDAR to refine habitat mapping by identifying high densities of potential hollow-bearing trees. We assessed if LiDAR from 2009 could be accurately used to detect tree heights, which would correlate to tree diameter at breast height (DBH), which in turn would identify trees that are more likely to be hollow-bearing. Here, we use habitat mapping of greater gliders (Petauroides spp.) in the Fraser Coast region of Australia as a case study. Across four sites, field surveys were conducted in 2023 to assess the tree height and density of large trees (>50 cm DBH per 1 km²) at 19 transects (n = 91). This was compared to outputs from individual tree detection derived from unsupervised classification using a local maximal filter and variable window size to identify treetops in freely available LiDAR. Tree height was measured with an accuracy of RMSE 5.75 m, and we were able to identify transects with large trees (>50 cm DBH), which were more likely hollow bearing. However, there was no statistical evidence to suggest that transects with a high density of large trees could be accurately identified based on LiDAR alone (>50 cm DBH p 0.2298). Despite this, we have demonstrated that freely accessible LiDAR and unsupervised machine learning techniques can be utilised to identify large, potentially hollow-bearing trees on a broad scale to refine habitat mapping for hollow-dependent species. It is important to develop geospatial analysis methods that are more accessible to land managers, as deep machine learning methods and current LiDAR can be computationally intensive and expensive. We propose a workflow using free and accessible geospatial analysis methods to identify large, potentially hollow-bearing trees and determine how to address some limitations in this geospatial approach. Full article

Scanning forests with LiDAR is an efficient method for conducting forest resource surveys, including estimating tree diameter at breast height (DBH), canopy height, and segmenting individual trees. This study uses three-dimensional (3D) forest test data and point cloud data simulated by the Helios++ V1.3.0 software, and proposes a voxelized trunk extraction algorithm to determine the trunk location and the vertical structure of single tree trunks in forest areas. Firstly, the voxel-based shape recognition algorithm is used to extract the trunk structure of tree point clouds, then the random sample consensus (RANSAC) algorithm is used to solve the vertical structure connectivity problem of tree trunks generated by the above method, and the Alpha Shapes algorithm is selected among various point cloud surface reconstruction algorithms to reconstruct the surface of tree point clouds. Then, building on the tree surface model, a light projection scene is introduced to locate the tree trunk coordinates at different heights. Finally, the convex hull of the trunk bottom is solved by the Graham scanning method. Accuracy assessments show that the proposed single-tree extraction algorithm and the forest vertical structure recognition algorithm, when applied within the light projection scene, effectively delineate the regions where the vertical structure distribution of single tree trunks is inconsistent. Full article

The rapid and precise acquisition of forest stand parameters is a key challenge in forest resource assessment. Terrestrial laser scanning (TLS) provides a fast and accurate method, but its accuracy is influenced by factors like tree segmentation parameters. This study focuses on Pinus tabuliformis plantations in the Caijiachuan watershed, Jixian, Shanxi, on the Loess Plateau. Based on field survey data, including tree number, height (H), diameter at breast height (DBH), and biomass, high-precision point cloud data were acquired using TLS. A comparative shortest path (CSP) algorithm was used for individual tree segmentation to investigate the effect of parameter selection on measurement accuracy. The results show that minimum tree height has a significant impact on segmentation accuracy. As the minimum tree height increased from 3.0 to 5.5 m, the recall rate (R) decreased while the precision (P) increased. The highest precision (F-score = 0.9470) and biomass estimation accuracy (0.9066) were obtained with a minimum tree height of 4.5 m, and the best extraction accuracies for H and DBH (0.9677 and 0.9518) were obtained at 5.0 m. Optimizing the minimum tree height parameter improves segmentation accuracy, thereby enhancing the use of TLS for soil and water conservation on the Loess Plateau. Full article

This study aimed to evaluate the role of trees outside forests on agricultural land (TOF-AL) in preserving the initial aboveground biomass (AGB) of forests within the agricultural landscape of Mongala province in the Democratic Republic of Congo. In 2024, tree inventories were conducted over four months in the forests and agricultural lands of Mongala province to analyse AGB. The effects of artisanal logging and charcoal production activities on the AGB conservation rate were considered. This study indicates that 78.3% of the trees encountered in agricultural lands were large-diameter trees (diameter at breast height (DBH) ≥ 60 cm). In forest areas, large-diameter trees accounted for 55.9% of tree density. The average AGBs are 66.8 Mg ha⁻¹ for TOF-AL and 373.5 Mg ha⁻¹ for forest trees. The AGB of TOF-AL accounts for 17.9% of the AGB of the total forest trees. The AGB conservation rates vary by region, with Lisala having the highest at 22.1%, Bumba the lowest at 11.2%, and Bongandanga at 20.5%. Artisanal logging and charcoal production reduce the AGB conservation rate of TOF-AL. The AGB conservation rate is positively correlated with the distances to major cities. These results prove that conserving trees in agricultural landscapes can reduce the AGB losses associated with slash-and-burn agriculture and contribute to mitigating climate change effects. Full article

Accurate forest monitoring and resource assessment are crucial for sustainable forest management, with tree diameter at breast height (DBH) serving as a key metric for tree growth assessment and carbon storage estimation. In this study, we developed a comprehensive mobile-LiDAR-based point cloud processing pipeline to segment individual trees and estimate the DBH of trees. We first conducted terrain extraction using a resolution-passing method combined with a cloth simulation filter. Then, by leveraging the vertical structural characteristics of trees and changes in point cloud density, we achieved high-performance tree trunk segmentation. On this basis, we deployed the Randomized Hough Transform algorithm to estimate the DBH of the trees. Finally, a large-scale experiment was conducted in a forest (Olympic Forest Park, Beijing, China) and we provided experimental results comparing our trunk segmentation and DBH estimation to ground-truth measurements recorded manually. Eventually, our results showed that 97.4% of the trees were accurately segmented, and the DBH estimation error was reduced to 3.2 cm, which shows that the proposed pipeline is able to achieve high-accuracy trunk segmentation and high-precision DBH estimation. Further, this research demonstrates that integrating MLS with SLAM technology can enhance the efficiency and accuracy of forest surveys, providing a valuable tool for future forest management strategies. Full article

(1) Background: Nature-based solutions (NbS), particularly through forest biomass, are crucial in mitigating climate change. While forest plantations play a critical role in carbon capture, the absence of species-specific biomass estimation models presents a significant challenge. This research focuses on developing allometric models to accurately estimate the aboveground biomass of Eucalyptus saligna Sm in Ecuador’s Lower Montane thorny steppe. (2) Methods: Conducted at the Tunshi Experimental Station of ESPOCH in Chimborazo, Ecuador, the research involved 46 trees to formulate biomass predictive models using both destructive and non-destructive methods. Sixteen generic models were tested using the ordinary least squares method. (3) Results: The most effective allometric equation for estimating six-year-old E. saligna biomass was Ln(B) = −0.952 + 1.97∗Ln(dbh), where B = biomass in kg/tree, and dbh = diameter at breast height in cm. This model represents a valuable contribution to improve biomass and carbon estimates in mitigation projects in Ecuador. (4) Conclusions: The tested models stand out for their simplicity, requiring only dbh as input, and demonstrate high accuracy and fit to contribute to the field of climate change mitigation. Full article

Understanding the characteristics of urban plant multidimensional diversity and urban green spaces (UGSs) landscape patterns is the central theme of urban ecology, providing theoretical support for UGSs management and biodiversity conservation. Taking Changchun, a provincial city, as an example, a total of 240 plots were surveyed using the stratified random sampling method. We studied the effects of the urban park construction period on plant multidimensional diversities, landscape patterns of green spaces, and their associations in Changchun City, Northeast China. The results indicated that total woody species and tree species diversity attributes were both the highest in the construction period of 2001–2020 and lowest in the construction period before 1940. However, shrub species diversity attributes were completely the opposite. Diameter at the breast height (DBH) diversity index (H_d) was the highest in the construction period before 1940 and lowest in the construction period of 2001–2020. However, the height diversity index (H_h) showed the opposite trend. Phylogenetic structures of total woody species and tree species showed divergent patterns in parks constructed before 1940 and 1940–2000 period, while that in 2001–2020 period could not be determined. In contrast, the phylogenetic structure of the shrub species clustered across all construction periods. Landscape pattern metrics varied significantly among different construction periods. Total Area (TA) was the highest in the construction period of 2001–2020. The structural equation model (SEM) revealed that construction periods exerted significant direct effects on both multidimensional diversities and landscape patterns of green spaces. Specifically, construction periods indirectly affected tree species diversity through structural diversity and influenced shrub species’ phylogenetic diversity through shrub species diversity. What is more, Patch Density (PD), Edge Density (ED), and Aggregation Index (AI) correlated with H_h, which had a direct effect on the Shannon–Wiener diversity index of tree species (H′_t). Overall, the results indicated that species diversity can be enhanced through regulating landscape patterns, rationally selecting tree species, and optimizing plant configuration. These above results can provide scientific references for the configuration of plant communities and selection of tree species in urban parks, and offer important guidance for urban biodiversity conservation and enhancement. Full article

Forest fine fuels are a crucial component of surface fuels and play a key role in igniting forest fires. However, despite nearly 20 years of long-term prescribed burning management on Zhaobi Mountain in Xinping County, Yunnan Province, China, there remains a lack of specific quantification regarding the effectiveness of fine fuel management in Pinus yunnanensis forests. In this study, 10 m × 10 m sample plots were established on Zhaobi Mountain following one year of growth after prescribed burning. The plots were placed in a prescribed burning (PB) area and an unburned control (UB) area. We utilized indicators such as forest stand characteristics, fine fuel physicochemical properties, and potential fire behavior parameters for evaluation. The results indicate that prescribed burning at one-year intervals significantly affects stand characteristics, particularly in metrics such as crown base height, diameter breast height, and fuel load (p < 0.05). However, the physical and chemical properties of fine fuels did not show significant differences. Notably, the mean range of spread (RS) of PB fuels downhill was 43.3% lower than that of UB fuels, and the mean flaming height (FH) was 35.2% lower. The fire line intensity was <750 kW/m, categorizing it as a low-intensity fire. These findings provide data on the composition of fine fuels and the variables of fire behavior affected by prescribed burning, demonstrating that low-intensity prescribed burns can regulate fine fuels in the understory and maintain a stable regional fire risk level. Full article

In the absence of long-term hydrologic records, field-measured hydrologic indicators are useful for inferring past wetland hydrologic conditions, which can support research, regulation, and restoration. Inflection points on the buttresses of pondcypress trees (Taxodium ascendens) are frequently used in west-central Florida to estimate cypress wetland high water levels, known as normal pool. However, little is known about how this indicator develops. A method to estimate tree age using diameter at breast height was developed for Florida pondcypress, which can be used by forested wetland managers to constrain the maximum potential age of hydrologic indicators in groups of cypress trees. This model was applied to a waterbody with a complex history of hydrologic alterations. The waterbody had two distinct populations of buttress inflection elevations, corresponding to historic versus current water level regimes. This represents one of the first documented instances in the literature where a waterbody showed multiple buttress inflection populations in the absence of soil subsidence. This work underscores the need to consider the development timelines when interpreting the hydrologic meaning of indicator elevations. Full article

Tropical forests are essential ecosystems recognized for their carbon sequestration and biodiversity benefits. As the world undergoes a simultaneous data revolution and climate crisis, accurate data on the world’s forests are increasingly important. Completely novel in approach, this study proposes a methodology encompassing two bespoke deep learning models: (1) a single encoder, double decoder (SEDD) model to generate a species segmentation map, regularized by a distance map in training, and (2) an XGBoost model that estimates the diameter at breast height (DBH) based on tree species and crown measurements. These models operate sequentially: RGB images from the ReforesTree dataset undergo preprocessing before species identification, followed by tree crown detection using a fine-tuned DeepForest model. Post-processing applies the XGBoost model and custom allometric equations alongside standard carbon accounting formulas to generate final sequestration estimates. Unlike previous approaches that treat individual tree identification as an isolated task, this study directly integrates species-level identification into carbon accounting. Moreover, unlike traditional carbon estimation methods that rely on regional estimations via satellite imagery, this study leverages high-resolution, drone-captured RGB imagery, offering improved accuracy without sacrificing accessibility for resource-constrained regions. The model correctly identifies 67% of trees in the dataset, with accuracy rising to 84% for the two most common species. In terms of carbon accounting, this study achieves a relative error of just 2% compared to ground-truth carbon sequestration potential across the test set. Full article

The diversity of commercial tree planting sites, with their distinct environmental conditions, directly influences tree growth and consequently impacts the wood properties in various ways. However, there is limited research evaluating the impact of these variations in nondestructive testing. Therefore, this study aimed to investigate whether edaphoclimatic variations affect parameters obtained through nondestructive tests conducted on standing trees. To this end, 30 specimens were selected from 3 Eucalyptus sp. clones, aged 1, 3, and 4 years, grown in 2 regions, totaling 540 trees. Tree development was monitored quarterly over 12 months. The tests included ultrasound propagation, drilling resistance, and penetration resistance, and the trees were measured for diameter at breast height (DBH) and height. Among the edaphoclimatic factors evaluated, only temperature and soil water storage differed statistically between the two study regions. The higher temperature and lower soil water storage in region 2 promoted tree growth, with these trees showing greater drilling resistance and higher longitudinal wave velocities. In addition, the influence of climatic factors was evidenced by the variation of wave propagation velocity throughout the year. Periods of lower water availability resulted in higher velocities, while periods of greater precipitation were associated with lower velocities. The research results showed that temperature and soil water storage had significant effects on tree growth (DBH and height), as well as ultrasound wave propagation velocity and drilling resistance. Full article

Forest resource surveys are of vital importance for grasping the current status of forest resources, formulating management strategies, and evaluating ecosystem functions. Traditional manual measurement methods have numerous limitations in complex forest environments. The emergence of LiDAR technology has provided a new approach. Backpack LiDAR has been increasingly applied due to its portability and flexibility. However, there is a lack of comprehensive research on the influence of different scanning routes on data quality and analysis results. In this study, forest plots of four tree species, namely Carya cathayensis, Cinnamomum camphora, Koelreuteria bipinnata, and Quercus acutissima in Chuzhou City, Anhui Province, were selected as the research objects. Six scanning routes were designed to collect point cloud data using backpack LiDAR. After preprocessing, including denoising and ground point classification, diameter at breast height (DBH) fitting and accuracy evaluation were carried out. The results indicated that the individual tree recognition rates of C. cathayensis, C. camphora, and K. bipinnata reached 100%, while that of Q. acutissima was between 64.71% and 78.07% and was significantly affected by the scanning route. The DBH fitting accuracy of each tree species varied among different routes. For example, C. cathayensis had high accuracy in routes 1 and 6, and C. camphora had high accuracy in routes 1 and 3. Tree species characteristics, scanning routes, and data processing methods jointly affected the DBH fitting accuracy. This study provides a basis for the application of backpack LiDAR in forest resource surveys. Although backpack LiDAR has advantages, it is still necessary to optimize data acquisition schemes targeting tree species characteristics and improve point cloud data processing algorithms to promote its in-depth application in the forestry field. Full article

Close-to-nature forest management is a sustainable forest management approach aimed at achieving a balance between ecological and economic benefits. The cultivation of future crop trees in the later successional stages following the removal of competitive trees is crucial for promoting positive development trajectories of succession. Understanding the dynamic process of growth investment strategies in future crop trees facilitates the rational planning of management cycles and scopes, ultimately enhancing the quality of tree cultivation. This study was conducted in a Pinus massoniana secondary forest with close-to-nature forest management in Ningbo City, Zhejiang Province, using handheld mobile laser scanning technology to precisely reconstruct the structure of future crop trees. Over a period of 2–5 years following the initial implementation of close-to-nature forest management, 3D point cloud data were collected annually from both managed and reference (non-managed) plots. Using these multi-temporal data, we analyzed the dynamics of the investment strategies, structural growth components, and crown competition of future crop trees. A linear mixed-effect model was applied to compare the temporal variations in these indices between the managed and control plots. Our results revealed that the height-to-diameter ratio of the future crop trees gradually declined over time, while the crown-to-diameter ratio initially increased and then decreased in the managed plots. These trends were significantly different from those observed in the control plots. Additionally, the height growth rates of the future crop trees in the managed plots were consistently lower than those in the control plots, whereas the crown and diameter at breast height (DBH) growth rates were higher. Furthermore, the crown gap area between the future crop trees and their neighboring trees gradually diminished, and the crown overlap progressively increased. These results suggest that the investment in height growth, initially driven by crown competition, shifted toward crown and DBH growth following close-to-nature forest management. In the initial stage after the removal of competitive trees, future crop trees benefited from ample crown radial space and minimal crown competition. However, as the crown radial space became increasingly limited, the future crop trees shifted their growth investment toward DBH to enhance mechanical stability and achieve a balanced tree structure. Understanding these dynamic processes and the underlying mechanisms of growth investment strategies contributes to predicting future forest community development, improving forest productivity, maintaining structural diversity, and ensuring sustainable forest management. Full article

Establishing an optimum range of inter-species spacing that reduces competition among trees and mitigates the effects of drought is a critical yet complex challenge in forest management. Stand density plays a crucial role in forest functioning by regulating resource allocation within individual trees. Higher stand densities have been shown to reduce sap velocities, indicating intensified competition for water and other resources. However, determining the precise spacing that minimizes competition while maintaining ecosystem balance remains unclear. In this study, conducted in temperate Norway spruce forests at an altitude range of 400–500 m in the Czech Republic, we propose a novel technique to define tree spacing that reduces competitive interactions. We used xylem sap flow residuals of an ordinary least square (OLS) regression model to filter out the effects of elevation and diameter at breast height (DBH) on field-measured sap flow for 101 planted Norway spruce trees with a DBH range of 40 ± 5 cm (≈90–100 years old). The model residuals allowed us to account for the most important driver of sap flow variability: tree density and its underlying effects on individual tree traits. To minimize the confounding effects of temporal and spatial variability, we used twelve consecutive daily measurements of sap flow (6 a.m. to 6 p.m.) taken at the start of the growing season. By constructing an experimental variogram, we quantified sap flow variability as a function of tree spacing. The results showed a steady sap flow pattern at tree densities of 12, 11, and 10 trees per 314 m² (equivalent to 350 ± 32 trees per hectare), corresponding to inter-tree spacing measurements of 5.12 m, 5.34 m, and 5.60 m, respectively. These findings suggest that when the N number of trees (median) per unit area (A) is in equilibrium with resource availability, increasing or decreasing the n number of trees may not significantly change competition levels (A; f(A) = N ± n). The size or deviation of n depends on the area to define the minimum and maximum thresholds or tolerance capacity for the number of trees allowed to be in the area. This technique—using a variogram of sap flow residuals to determine tree spacing—can be periodically applied, such as every 10–15 years, and adapted for different elevation gradients (e.g., within 100 m intervals). It offers a practical tool for forest managers and policymakers, guiding thinning and planting strategies to enhance forest resilience in the face of water-stress conditions. Full article

Effective classification of wood and leaf points from terrestrial laser scanning (TLS) point clouds is critical for analyzing and estimating forest attributes such as diameter at breast height (DBH), above-ground biomass (AGB), and wood volume. To address this, we introduce the Wood–Leaf Classification Network (WLC-Net), a deep learning model derived from PointNet++, designed to differentiate between wood and leaf points within tree point clouds. WLC-Net enhances classification accuracy, completeness, and speed by incorporating linearity as an inherent feature, refining the input–output framework, and optimizing the centroid sampling technique. We trained and evaluated WLC-Net using datasets from three distinct tree species, totaling 102 individual tree point clouds, and compared its performance against five existing methods including PointNet++, DGCNN, Krishna Moorthy’s method, LeWoS, and Sun’s method. WLC-Net achieved superior classification accuracy, with overall accuracy (OA) scores of 0.9778, 0.9712, and 0.9508; the mean Intersection over Union (mIoU) scores of 0.9761, 0.9693, and 0.9141; and F1-scores of 0.8628, 0.7938, and 0.9019, respectively. The model also demonstrated high efficiency, processing an average of 102.74 s per million points. WLC-Net has demonstrated notable advantages in wood–leaf classification, including significantly enhanced classification accuracy, improved processing efficiency, and robust applicability across diverse tree species. These improvements stem from its innovative integration of linearity in the model architecture, refined input–output framework, and optimized centroid sampling technique. In addition, WLC-Net also exhibits strong applicability across various tree point clouds and holds promise for further optimization. Full article