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The presence of sufficient natural regeneration in mature forests is regarded as a pivotal criterion for their future stability, ensuring seamless reforestation following final harvesting operations or forest calamities. Consequently, forest regeneration is typically quantified as part of forest inventories to monitor its occurrence and development over time. Light detection and ranging (LiDAR) technology, particularly ground-based LiDAR, has emerged as a powerful tool for assessing typical forest inventory parameters, providing high-resolution, three-dimensional data on the forest structure. Therefore, it is logical to attempt a LiDAR-based quantification of forest regeneration, which could greatly enhance area-wide monitoring, further supporting sustainable forest management through data-driven decision making. However, examples in the literature are relatively sparse, with most relevant studies focusing on an indirect quantification of understory density from airborne LiDAR data (ALS). The objective of this study is to develop an accurate and reliable method for estimating regeneration coverage from data obtained through personal laser scanning (PLS). To this end, 19 forest inventory plots were scanned with both a personal and a high-resolution terrestrial laser scanner (TLS) for reference purposes. The voxelated point clouds obtained from the personal laser scanner were converted into raster images, providing either the canopy height, the total number of filled voxels (containing at least one LiDAR point), or the ratio of filled voxels to the total number of voxels. Local maxima in these raster images, assumed to be likely to contain tree saplings, were then used as seed points for a raster-based tree segmentation, which was employed to derive the final regeneration coverage estimate. The results showed that the estimates differed from the reference in a range of approximately −10 to +10 percentage points, with an average deviation of around 0 percentage points. In contrast, visually estimated regeneration coverages on the same forest plots deviated from the reference by between −20 and +30 percentage points, approximately −2 percentage points on average. These findings highlight the potential of PLS data for automated forest regeneration quantification, which could be further expanded to include a broader range of data collected during LiDAR-based forest inventory campaigns. [ABSTRACT FROM AUTHOR]

The determination of diameter at breast height (DBH) is critical in forestry, serving as a key metric for deriving various parameters, including tree volume. Light Detection and Ranging (LiDAR) technology has been increasingly employed in forest inventories, and the development of cost-effective, user-friendly smartphone and tablet applications (apps) has expanded its broader use. Among these are augmented reality (AR) apps, which have already been tested on mobile devices for their accuracy in measuring forest attributes. In February 2024, Apple introduced the Mixed-Reality Interface (MRITF) via the Apple Vision Pro (AVP), offering sensor capabilities for field data collection. In this study, two apps using the AVP were tested for DBH measurement on 182 trees across 22 sample plots in a near-natural forest, against caliper-based reference measurements. Compared with the reference measurements, both apps exhibited a slight underestimation bias of −1.00 cm and −1.07 cm, and the root-mean-square error (RMSE) was 3.14 cm and 2.34 cm, respectively. The coefficient of determination (R2) between the reference data and the measurements obtained by the two apps was 0.959 and 0.978. The AVP demonstrated its potential as a reliable field tool for DBH measurement, performing consistently across varying terrain. [ABSTRACT FROM AUTHOR]

Natural broadleaf forests (NBFs) are the most abundant zonal vegetation type in subtropical regions. Understanding the mechanisms influencing stand productivity in NBFs is important for developing "nature-based" solutions for climate change mitigation. However, minimal research has captured the effects of nonlinearities and feature interactions that often have nonlinear impacts on stand productivity and influencing factors. To address this research gap, we used continuous forest inventory data, and a machine learning model for stand productivity of NBFs was constructed. Subsequently, through leveraging the interpretable machine learning framework of the SHapley Additive explanation (SHAP) and partial dependence plot, we determined global and local explanations of the influencing factors of stand productivity. Our findings indicate the following: (1) The Autogluon model performed the strongest based on R2, RMSE, and rRMSE metrics. (2) The basal area (BA), neighborhood comparison of diameter at breast height (NC), and stand age (AGE) were the key influencing factors. Stand productivity increased with increasing BA and decreased with increasing NC and AGE. BA was maintained above 15 m2ha−1 and NC was maintained below 0.45, which represent favorable conditions for NBFs to maintain optimal growth. (3) SHAP interaction values were calculated to determine the effects of the five major interactions on stand productivity. Our study provides a reference for the sustainable management of NBFs, thereby highlighting the important role of forests in mitigating climate change. [ABSTRACT FROM AUTHOR]

The concept of tree or stem form has been central to forest research for over a century, playing a vital role in accurately assessing tree growth, volume, and biomass. The form factor is an essential component for expressing the shape of a tree, enabling more accurate volume estimation, which is vital for sustainable forest management and planning. Despite its simplicity, flexibility, and advantages in volume estimation, the form factor has received less attention compared to other measures like taper equations and form quotient. This review summarizes the concept, theories, and measures of stem form, and describes the factors influencing its variation. It focuses on the form factor, exploring its types, parameterization, and models in the context of various tropical species and geographic conditions. The review also discusses the use of the form factor in volume estimation and the issues with using default or generic values. The reviewed studies show that tree stem form and form factor variations are influenced by multiple site, tree, and stand characteristics, including site quality, soil type, climate conditions, tree species, age, crown metrics, genetic factors, stand density, and silviculture. The breast height form factor is the most adopted among the three common types of form factors due to its comparative benefits. Of the five most tested form factor functions for predicting tree form factors, Pollanschütz's function is generally considered the best. However, its performance is often not significantly different from other models. This review identifies the "Hohenadl" method and mixed-effects modelling as overlooked yet potentially valuable approaches for form factor modelling. Using the form factor, especially by diameter or age classes, can enhance tree volume estimation, surpassing volume equations. However, relying on default or generic form factors can lead to volume and biomass estimation errors of up to 17–35%, underscoring the need to limit variation sources in form factor modelling and application. Further recommendations are provided for improving the statistical techniques involved in developing form factor functions. [ABSTRACT FROM AUTHOR]

Tree species are important factors affecting the carbon sequestration capacity of forests and maintaining the stability of ecosystems, but trees are widely distributed spatially and located in complex environments, and there is a lack of large-scale regional tree species classification models for remote sensing imagery. Therefore, many studies aim to solve this problem by combining multivariate remote sensing data and proposing a machine learning model for forest tree species classification. However, satellite-based laser systems find it difficult to meet the needs of regional forest species classification characters, due to their unique footprint sampling method, and SAR data limit the accuracy of species classification, due to the problem of information blending in backscatter coefficients. In this work, we combined Sentinel-1 and Sentinel-2 data to construct a machine learning tree classification model based on optical features, vegetation spectral features, and PolSAR polarization observation features, and propose a forest tree classification feature selection method featuring the Hilbert–Huang transform for the problem of mixed information on the surface of SAR data. The PSO-RF method was used to classify forest species, including four temperate broadleaf forests, namely, aspen (Populus L.), maple (Acer), peach tree (Prunus persica), and apricot tree (Prunus armeniaca L.), and two coniferous forests, namely, Chinese pine (Pinus tabuliformis Carrière) and Mongolian pine (Pinus sylvestris var. mongolica Litv.). In this study, some experiments were conducted using two Sentinel-1 images, four Sentinel-2 images, and 550 measured forest survey sample data points pertaining to the forested area of Fuxin District, Liaoning Province, China. The results show that the fusion model constructed in this study has high accuracy, with a Kappa coefficient of 0.94 and an overall classification accuracy of 95.1%. In addition, this study shows that PolSAR data can play an important role in forest tree species classification. In addition, by applying the Hilbert–Huang transform to PolSAR data, other feature information that interferes with the perceived vertical structure of forests can be suppressed to a certain extent, and its role in the classification of forest species, combined with PolSAR, should not be ignored. [ABSTRACT FROM AUTHOR]

Remote sensing (RS)-based forest inventories are becoming increasingly common in forest management. However, practical applications often require subsequent optimisation steps. One of the most popular RS-based forest inventory methods is the two-phase inventory with regression estimator, commonly referred to as the area-based approach (ABA). There are many sources of variation that contribute to the overall performance of this method. One of them, which is related to the core aspect of this method, is the spatial co-registration error between ground measurements and RS data. This error arises mainly from the imperfection of the methods for positioning the sample plots under the forest canopy. In this study, we investigated how this positioning accuracy affects the area-based growing stock volume (GSV) estimation under different forest conditions and sample plot radii. In order to analyse this relationship, an artificial co-registration error was induced in a series of simulations and various scenarios. The results showed that there were minimal differences in ABA inventory performance for displacements below 4 m for all stratification groups except for deciduous sites, where sub-metre plot positioning accuracy was justified, as site- and terrain-related factors had some influence on GSV estimation error (r up to 0.4). On the other hand, denser canopy and spatially homogeneous stands mitigated the negative aspects of weaker GNSS positioning capabilities under broadleaved forest types. In the case of RMSE, the results for plots smaller than 400 m2 were visibly inferior. The BIAS behaviour was less strict in this regard. Knowledge of the actual positioning accuracy as well as the co-registration threshold required for a particular stand type could help manage and optimise fieldwork, as well as better distinguish sources of statistical uncertainty. [ABSTRACT FROM AUTHOR]

Timely and accurate tree species mapping is crucial for forest resource inventory, supporting management, conservation biology, and ecological restoration. This study utilized Sentinel-1 and Sentinel-2 data to classify five dominant tree species in Chengde and Beijing. To effectively capture the influence of multi-temporal data, data were acquired in March, June, September, and December 2020, extracting various features, including bands, spectral indices, texture features, and topographic variables. The optimal input variable combination was explored using 1519 field survey samples for training and testing datasets. Classification employed Random Forest, XGBoost, and deep learning models, with performance evaluated through out-of-bag estimation and cross-validation. The XGBoost model achieved the highest accuracy of 81.25% (kappa = 0.74) when using Sentinel-1 and Sentinel-2 bands, indices, texture features, and DEM data. Results demonstrate the effectiveness of using Sentinel data for tree species classification and emphasize the value of machine learning algorithms. This study underscores the potential of combining synthetic aperture radar (SAR) and optical data for large-scale tree species classification, with significant implications for forest monitoring and management. [ABSTRACT FROM AUTHOR]

Gap dynamics are driving many important processes in the development of temperate forest ecosystems. What remains largely unknown is how often the regeneration processes initialized by endogenous mortality of dominant and co-dominant canopy trees take place. We conducted a study in the high mountain forests of the Central Western Carpathians, naturally dominated by the Norway spruce. Based on the repeated forest inventories in two localities, we quantified the structure and amount of deadwood, as well as the associated mortality of standing dead canopy trees. We determined the basic specific gravity of wood and anatomical changes in the initial phase of wood decomposition. The approach for estimating the rate of gap formation and the number of canopy trees per unit area needed for intentional gap formation was formulated based on residence time analysis of three localities. The initial phase of gap formation (standing dead tree in the first decay class) had a narrow range of residence values, with a 90–95% probability that gap age was less than 10 or 13 years. Correspondingly, a relatively constant absolute number of 12 and 13 canopy spruce trees per hectare died standing in 10 years, with a mean diameter reaching 50–58 cm. Maximum diameters trees (70–80 cm) were represented by 1–4 stems per hectare. The values of the wood-specific gravity of standing trees were around 0.370–0.380 g.cm−3, and varied from 0.302 to 0.523 g.cm−3. Microscopically, our results point out that gap formation is a continuous long-lasting process, starting while canopy trees are living. We observed early signs of wood degradation and bacteria, possibly associated with bark beetles, that induce a strong effect when attacking living trees with vigorous defenses. New information about the initial phase of gap formation has provided a basis for the objective proposal of intervals and intensities of interventions, designed to promote a diversified structure and the long-term ecological stability of the mountain spruce stands in changing climate conditions. [ABSTRACT FROM AUTHOR]

To describe the characteristics and dynamics of the Picea jezoensis (PJ) community, a survey of 48 forest plots containing PJ trees was conducted in Northeast China. Methods of community grouping, analysis of variance, and linear regression, and indicators of relative basal area (RBA, reflecting the dominance degree of species in the community) and temporary stability (TS, reflecting the stability of forest plant community), were employed. The objectives were to describe the dominant species composition and environment of the PJ community and to quantify the changes in PJ's dominance status. Communities with PJ trees were divided into four groups: PJ as the first dominant species; PJ as the second dominant species; PJ being co-dominant but ranked third or lower; and PJ as a non-dominant species. Among them, the PJ community (i.e., where PJ is the first dominant species) occupied sites at the highest elevation with a mean value of 1408 m, on gentle slopes ≤ 10°, where the tree species occurring with PJ mainly included Betula costata and Abies nephrolepis. A linear relationship was found between the TS and RBA. The threshold for PJ shifting between being the first and second dominant species in the community was RBA = 0.387. This study provides a scientific basis for judging the attributes and status of PJ community and the protection and restoration of PJ forests. These findings also provide comparable information for ecological research and conservation of PJ forests in other areas. [ABSTRACT FROM AUTHOR]

Reliable prediction of tree stem volume is crucial for effective forest management and ecological assessment. Traditionally, regression models have been applied to estimate forest biometric variables, yet they often fall short when handling the complex, non-linear patterns typical of biological data, potentially introducing biases and errors. Tree stem volume, a critical metric in forest biometrics, is generally estimated through easily measured parameters such as diameter at breast height (d) and total tree height (h). This study investigates advanced machine learning (ML) techniques—Extreme Gradient Boosting (XGBoost), epsilon-Support Vector Regression (ε-SVR), and Random Forest regression (RFr)—to predict the stem volume of European black pine (Pinus nigra) on Mount Olympus, Greece, using basic field measurements. Machine learning (ML) approaches demonstrated substantial improvements in prediction accuracy compared to traditional non-linear regression-based models (RMs). Notably, XGBoost significantly enhanced predictive performance by reducing the Furnival index (FI) by as much as 42.3% (from 1.1859 to 0.1056) and 21.3% (from 0.1475 to 0.1161) in the test and fitting datasets, respectively, for the single-entry model. For the double-entry model, XGBoost achieved FI reductions of 40.5% (from 0.1136 to 0.0676) and 41.3% (from 0.1219 to 0.0715) in the test and fitting datasets, respectively. These findings highlight the potential of ML models to improve the accuracy of forest inventory predictions, thereby supporting more effective and data-driven forest management strategies. [ABSTRACT FROM AUTHOR]

Monitoring forest stands using sampling techniques offers a valuable alternative to conventional forest condition assessment methods in Central Europe. While these designs are optimized for assessing production parameters, their effectiveness for structural characteristics remains unclear. This study evaluates various plot designs to determine their reliability in estimating structural diversity indices, including the Gini index, Artenprofile index, and Shannon index. We compared ten fixed-radius (FR) sampling designs (plot sizes: 50–1250 m2) and a concentric circle (CC) design (500 m2) employed at the Mendel University Forest Enterprise (Křtiny, Czech Republic). The CC design proved adequate for assessing production parameters and structural diversity indices like Artenprofile and Shannon. However, it showed significant limitations for the Gini index (p < 0.01), due to a smaller number of sampled trees. For the Gini index, fixed-radius plots of at least 150 m2, with 200 m2 being the most cost-effective size, provided the most reliable estimates. Interestingly, the CC design may also be less suitable for production parameters, where smaller fixed-radius plots (50 m2) were more effective, requiring fewer total samples despite the need for more plots. [ABSTRACT FROM AUTHOR]

Wood–leaf separation from forest LiDAR point clouds is a challenging task due to the complex and irregular structures of tree canopies. Traditional machine vision and deep learning methods often struggle to accurately distinguish between fine branches and leaves. This challenge arises primarily from the lack of suitable features and the limitations of existing position encodings in capturing the unique and intricate characteristics of forest point clouds. In this work, we propose an innovative approach that integrates Local Surface Features (LSF) and a Position Encoding (PosE) module within the Point Transformer (PT) network to address these challenges. We began by preprocessing point clouds and applying a machine vision technique, supplemented by manual correction, to create wood–leaf-separated datasets of forest point clouds for training. Next, we introduced Point Feature Histogram (PFH) to construct LSF for each point network input, while utilizing Fast PFH (FPFH) to enhance computational efficiency. Subsequently, we designed a PosE module within PT, leveraging trigonometric dimensionality expansion and Random Fourier Feature-based Transformation (RFFT) for nuanced feature analysis. This design significantly enhances the representational richness and precision of forest point clouds. Afterward, the segmented branch point cloud was used to model tree skeletons automatically, while the leaves were incorporated to complete the digital twin. Our enhanced network, tested on three different types of forests, achieved up to 96.23% in accuracy and 91.51% in mean intersection over union (mIoU) in wood–leaf separation, outperforming the original PT by approximately 5%. This study not only expands the limits of forest point cloud research but also demonstrates significant improvements in the reconstruction results, particularly in capturing the intricate structures of twigs, which paves the way for more accurate forest resource surveys and advanced digital twin construction. [ABSTRACT FROM AUTHOR]

In Republic of Korea, the digital transformation of forest data has emerged as a critical priority at the governmental level. To support this effort, numerous case studies have been conducted to collect and analyze forest data. This study evaluated the accuracy of forest resource assessment methods using terrestrial laser scanning (TLS) and backpack personal laser scanning (BPLS) under Leaf-on and Leaf-off conditions in the Gari Mountain Forest Management Complex, Hongcheon, Republic of Korea. The research was conducted across six sample plots representing low, medium, and high stand densities, dominated by Larix kaempferi and Pinus koraiensis. Conventional field survey methods and LiDAR technologies were used to compare key forest attributes such as tree height and volume. The results revealed that Leaf-off LiDAR data exhibited higher accuracy in capturing tree height and canopy structures, particularly in high-density plots. In contrast, during the Leaf-on season, measurements of understory vegetation and lower canopy were hindered by foliage obstruction, reducing precision. Seasonal differences significantly impacted LiDAR measurement accuracy, with Leaf-off data providing a clearer and more reliable representation of forest structures. This study underscores the necessity of considering seasonal conditions to improve the accuracy of LiDAR-derived metrics. It offers valuable insights for enhancing forest inventory practices and advancing the application of remote sensing technologies in forest management. [ABSTRACT FROM AUTHOR]

Forest disturbance is one of the main drivers of forest carbon flux change. How to accurately estimate the carbon flux caused by forest disturbance is an important research problem. In a previous study, the authors proposed a Grid-based Carbon Accounting (GCA) model that used remote sensing data to estimate forest carbon fluxes in North Carolina from 1986 to 2010. However, the original model was unable to track legacy emissions from previously harvested wood products and was unable to consider forest growth conditions before and after forest disturbance. This paper made some improvements to the original GCA model to enable it to track fluxes between all major aboveground live carbon pools, including pre-disturbance growth and growth of undisturbed forests, which were not included in the initial model. Based on existing timber product output (TPO) survey data and annual TPO records inversed from remote sensing data, we also worked to clarify the distribution ratios of removed C between slash and different wood product pools. Specifically, the average slash ratio for North Carolina was calculated from the difference between the C removed and the C flowing into the wood product as calculated from TPO survey data. County- and year-specific ratios were then calculated using the annual TPO records obtained from remote sensing and TPO survey data, dividing the removed remaining C into pools P1, P10, and P100, which were then applied to each 30 m pixel based on the county and year to which the pixel belonged. After compensating for these missing legacy emissions and adjusting forest growth rates from Forest Inventory and Analysis (FIA) data, we estimated a net carbon sink of 218.1 Tg of the flux associated with live aboveground biomass and harvested wood products from North Carolina woodlands over the 25-year study period (1986–2010). This estimate is close to the greenhouse gas emission and sink data provided by the U.S. Department of Agriculture for North Carolina and is comparable to estimates reported by several other studies. [ABSTRACT FROM AUTHOR]

In forest resource surveys, using sampling methods to estimate aboveground carbon stock (ACS) can significantly reduce survey costs. This study improves the accuracy of ACS estimation by optimizing the stratified sampling design. The sampling process was divided into two stages: stratification and intra-stratum sampling. For stratification, remote sensing features were used as stratification variables, and a spatial clustering stratification method was introduced. For intra-stratum sampling, a composite method, Spatially Correlated Poisson Disk Sampling (SCPDS), was proposed. Using Random Forest (RF) and the sample points selected by SCPDS, the ACS was estimated and compared with traditional sampling methods for Pinus densata in Shangri-La, Yunnan, China. The results showed that (1) by selecting effective stratification variables (e.g., texture features), the required sample size was reduced by up to 19.35% compared to that of simple random sampling; (2) the Ward clustering method greatly improved stratification heterogeneity; (3) for intra-stratum sampling, the SCPDS method ensured spatial independence within strata, particularly at low sampling rates (1%–5%), where its error was significantly lower than that of other methods, indicating greater stability and improved accuracy; (4) the SCPDS-based model achieved the best fitting accuracy, with R2 = 0.886. The total carbon stock of Pinus densata using RF was 7,872,787.5 t, closely matching forest management inventory (FMI) data. Through sampling, even with a relatively small sample size, the representative plots can still accurately reflect ACS estimates that are consistent with those derived from large-scale plot surveys. Thus, the optimized stratified sampling method effectively reduced sampling costs while significantly enhancing the stability and accuracy of the results. [ABSTRACT FROM AUTHOR]

Forest plot surveys are vital for monitoring forest resource growth, contributing to their sustainable development. The accuracy and efficiency of these surveys are paramount, making technological advancements such as Simultaneous Localization and Mapping (SLAM) crucial. This study investigates the application of SLAM technology, utilizing LiDAR (Light Detection and Ranging) and monocular cameras, to enhance forestry plot surveys. Conducted in three 32 × 32 m plots within the Tibet Autonomous Region of China, the research compares the efficacy of LiDAR-based and visual SLAM algorithms in estimating tree parameters such as diameter at breast height (DBH), tree height, and position, alongside their adaptability to forest environments. The findings revealed that both types of algorithms achieved high precision in DBH estimation, with LiDAR SLAM presenting a root mean square error (RMSE) range of 1.4 to 1.96 cm and visual SLAM showing a slightly higher precision, with an RMSE of 0.72 to 0.85 cm. In terms of tree position accuracy, the three methods can achieve tree location measurements. LiDAR SLAM accurately represents the relative positions of trees, while the traditional and visual SLAM systems exhibit slight positional offsets for individual trees. However, discrepancies arose in tree height estimation accuracy, where visual SLAM exhibited a bias range from −0.55 to 0.19 m and an RMSE of 1.36 to 2.34 m, while LiDAR SLAM had a broader bias range and higher RMSE, especially for trees over 25 m, attributed to scanning angle limitations and branch occlusion. Moreover, the study highlights the comprehensive point cloud data generated by LiDAR SLAM, useful for calculating extensive tree parameters such as volume and carbon storage and Tree Information Modeling (TIM) through digital twin technology. In contrast, the sparser data from visual SLAM limits its use to basic parameter estimation. These insights underscore the effectiveness and precision of SLAM-based approaches in forestry plot surveys while also indicating distinct advantages and suitability of each method to different forest environments. The findings advocate for tailored survey strategies, aligning with specific forest conditions and requirements, enhancing the application of SLAM technology in forestry management and conservation efforts. [ABSTRACT FROM AUTHOR]

The sources of uncertainty in wall-to-wall AGB maps propagate from the tree to pixel, but uncertainty due to forest cover mapping is rarely incorporated into the error propagation process. This study aimed to (1) elaborate an analytical procedure to incorporate forest-mapping-related uncertainty into the error propagation from plot and pixel predictions; (2) develop a stratified estimator with a model-assisted estimator for small and large areas; and (3) estimate the effect of ignoring the mapping uncertainty on the confidence intervals (CIs) for totals. Data consist of a subset of the Brazilian national forest inventory (NFI) database, comprising 75 counties that, once aggregated, served as strata for the stratified estimator. On-ground data were gathered from 152 clusters (plots) and remotely sensed data from Landsat-8 scenes. Four major contributions are highlighted. First, we describe how to incorporate forest-mapping-related uncertainty into the CIs of any forest attribute and spatial resolution. Second, stratified estimators perform better than non-stratified estimators for forest area estimation when the response variable is forest/non-forest. Comparing our stratified estimators, this study indicated greater precision for the stratified estimator than for the regression estimator. Third, using the ratio estimator, we found evidence that the simple field plot information provided by the NFI clusters is sufficient to estimate the proportion forest for large regions as accurately as remote-sensing-based methods, albeit with less precision. Fourth, ignoring forest-mapping-related uncertainty erroneously narrows the CI width as the estimate of proportion forest area decreases. At the small-area level, forest-mapping-related uncertainty led to CIs for total AGB as much as 63% wider in extreme cases. At the large-area level, the CI was 5–7% wider. [ABSTRACT FROM AUTHOR]

Three raster-based (RB) and one point cloud-based (PCB) algorithms were tested to segment individual Aleppo pine trees and extract their tree height (H) and crown diameter (CD) using two types of point clouds generated from two different techniques: (1) Low-Density (≈1.5 points/m2) Airborne Laser Scanning (LD-ALS) and (2) photogrammetry based on high-resolution unmanned aerial vehicle (UAV) images. Through intensive experiments, it was concluded that the tested RB algorithms performed best in the case of UAV point clouds (F1-score > 80.57%, H Pearson's r > 0.97, and CD Pearson´s r > 0.73), while the PCB algorithm yielded the best results when working with LD-ALS point clouds (F1-score = 89.51%, H Pearson´s r = 0.94, and CD Pearson´s r = 0.57). The best set of algorithm parameters was applied to all plots, i.e., it was not optimized for each plot, in order to develop an automatic pipeline for mapping large areas of Mediterranean forests. In this case, tree detection and height estimation showed good results for both UAV and LD-ALS (F1-score > 85% and >76%, and H Pearson´s r > 0.96 and >0.93, respectively). However, very poor results were found when estimating crown diameter (CD Pearson´s r around 0.20 for both approaches). [ABSTRACT FROM AUTHOR]

Climate variations in temperature and precipitation significantly impact forest productivity. Precipitation influences the physiology and growth of species, while temperature regulates photosynthesis, respiration, and transpiration. This study developed bioclimatic models to assess how climate change will affect the carbon density of aboveground biomass (cdAGB) in Mexico's coniferous forests for 2050 and 2070. We used cdAGB data from the National Forest and Soils Inventory (INFyS) of Mexico and 19 bioclimatic variables from WorldClim ver. 2.0. The best predictors of cdAGB were obtained using machine learning techniques with the "caret" library in R. The model was trained with 80% of the data and validated with the remaining 20% using Generalized Linear Models (GLMs). Current cdAGB prediction maps were generated using the best predictors. Future cdAGB was calculated with the average of three general circulation models (GCMs) of future climate projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5), under four Representative Concentration Pathways (RCPs): 2.6, 4.5, 6.0, and 8.5 W/m2. The results indicate cdAGB losses in all climate scenarios, reaching up to 15 Mg C ha−1, and could occur under the RCP 8.5 scenario by 2070 in the central region of the country. Temperature-related variables are more important than precipitation variables. Bioclimatic variables can explain up to 20% of the total variance in cdAGB. The temperature in the study area is expected to increase by 2.66 °C by 2050 and 3.36 °C by 2070, while precipitation is expected to fluctuate by ±10% relative to the current values, which could geographically redistribute the cdAGB of the country's coniferous forests. These findings underscore the need for forest management to focus not only on biodiversity conservation but also on the carbon storage capacity of these ecosystems. [ABSTRACT FROM AUTHOR]

Tree canopy cover is an important forest inventory parameter and a critical component for the in-depth mapping of forest fuels. This research examines the potential of employing single-date Sentinel-2 multispectral imagery, combined with contextual spatial information, to classify areas based on their tree cover density using Random Forest classifiers. Three spatial information extraction methods are investigated for their capacity to acutely detect canopy cover: two based on Gray-Level Co-Occurrence Matrix (GLCM) features and one based on segment statistics. The research was carried out in three different biomes in Greece, in a total study area of 23,644 km2. Three tree cover classes were considered, namely, non-forest (cover < 15%), open forest (cover = 15%–70%), and closed forest (cover ≥ 70%), based on the requirements set for fuel mapping in Europe. Results indicate that the best approach identified delivers F1-scores ranging 70%–75% for all study areas, significantly improving results over the other alternatives. Overall, the synergistic use of spectral and spatial features derived from Sentinel-2 images highlights a promising approach for the generation of tree cover density information layers in Mediterranean regions, enabling the creation of additional information in support of the detailed mapping of forest fuels. [ABSTRACT FROM AUTHOR]

Traditional field inventories have been the standard method for collecting detailed forest attribute data. However, these methods are often time-consuming, labor-intensive, and costly, especially for large areas. In contrast, remote sensing technologies, such as unmanned aerial vehicles (UAVs), have become viable alternatives for collecting forest structure data, providing high-resolution images, precision, and the ability to use various sensors. To explore this trend, a bibliometric review was conducted using the Scopus database to examine the evolution of scientific publications and assess the current state of research on using UAVs to estimate dendrometric variables in forest ecosystems. A total of 454 studies were identified, with 199 meeting the established inclusion criteria for further analysis. The findings indicated that China and the United States are the leading contributors to this research domain, with a notable increase in journal publications over the past five years. The predominant focus has been on planted forests, particularly utilizing RGB sensors attached to UAVs for variable estimation. The primary variables assessed using UAV technology include total tree height, DBH, above-ground biomass, and canopy area. Consequently, this review has highlighted the most influential studies in the field, establishing a foundation for future research directions. [ABSTRACT FROM AUTHOR]

This study establishes a climate-sensitive transition matrix growth model and predicts forest growth under different carbon emission scenarios (representative concentration pathways RCP2.6, RCP4.5, and RCP8.5) over the next 40 years. Data from the Eighth (2013) and Ninth (2019) National Forest Resource Inventories in Chongqing and climate data from Climate AP are utilized. The model is used to predict forest growth and compare the number of trees, basal area, and stock volume under different climate scenarios. The results show that the climate-sensitive transition matrix growth model has high accuracy. The relationships between the variables and forest growth, mortality, and recruitment correspond to natural succession and growth. Although the number of trees, basal area, and stock volume do not differ significantly for different climate scenarios, the forest has sufficient seedling regeneration and large-diameter trees. The growth process aligns with succession, with pioneer species being replaced by climax species. The proposed climate-sensitive transition matrix growth model fills the gap in growth models for natural secondary forests in Chongqing and is an accurate method for predicting forest growth. The model can be used for long-term prediction of forest stands to understand future forest growth trends and provide reliable references for forest management. Forest growth can be predicted for different harvesting intensities to determine the optimal intensity to guide natural forest management in Chongqing City. The results of this study can help formulate targeted forest management policies to deal more effectively with climate change and promote sustainable forest health. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this article was to use the Random Forest method and nonlinear mixed-effects method to develop a model for determining tree height–diameter at breast height (DBH) for a natural coniferous and broad-leaved mixed forest in Jilin Province and to compare the advantages and disadvantages of the two methods to provide a basis for forest management practice. Method: Based on the Chinese national forest inventory data, the Random Forest method and nonlinear mixed-effects method were used to develop a tree height–DBH model for a natural coniferous and broad-leaved mixed forest in Jilin Province. Results: The Random Forest method performed well on both the fitting set and validation set, with an R2 of 0.970, MAE of 0.605, and RMSE of 0.796 for the fitting set and R2 of 0.801, MAE of 1.44 m, and RMSE of 1.881 m for the validation set. Compared with the nonlinear mixed-effects method, the Random Forest model improved R2 by 33.83%, while the MAE and RMSE decreased by 67.74% and 66.44%, respectively, in the fitting set; the Random Forest model improved R2 by 9.88%, while the MAE and RMSE decreased by 14.38% and 12.05%, respectively, in the validation set. Conclusions: The tree height–DBH model constructed based on the Random Forest method had higher prediction accuracy for a natural coniferous and broad-leaved mixed forest in Jilin Province and had stronger adaptability for higher-dimensional data, which can be used for tree height prediction in the study area. [ABSTRACT FROM AUTHOR]

Bamboo forests exhibit a unique efficient growth pattern that makes them invaluable in reducing atmospheric CO2 levels. Additionally, bamboo forests offer a diverse range of products, thus holding the potential to bolster local income. Despite these benefits, the comprehensive assessment of bamboo forests' potential in both carbon abatement and improving local income enhancement has been hindered by the absence of a detailed bamboo biomass map. In this study, we address this gap by amalgamating a bamboo aboveground biomass (AGB) map covering three prominent producing provinces in southern China, utilizing multi-source remote sensing datasets. The results not only demonstrate a satisfactory consistency with China's Ninth National Forest Inventory but also provide a more detailed spatial distribution. Based on this AGB estimation, we project an approximately threefold potential increase in annual bamboo culm harvest from existing bamboo forests. This represents a significant opportunity for expanding carbon abatement efforts, elevating local income levels, and facilitating the production of bamboo-derived biofuels. Furthermore, the adoption of an optimized management strategy has the potential to further enhance bamboo production. This study generates the first high-resolution bamboo AGB map and underscores the substantial potential of China's bamboo forests in contributing to carbon sequestration and improving local income. The favorable income generated for local residents can serve as a compelling incentive for the implementation of sustainable forest management practices, offering a promising pathway toward achieving carbon-related objectives within the forestry sector and providing necessary support for forestry designation projects. [ABSTRACT FROM AUTHOR]

The design of a sampling unit, whether a simple plot or a subplot within a clustered structure, including shape and size, has received little attention in inferential forestry research. The use of auxiliary variables from remote sensing impacts the precision of estimators from both model-assisted and model-based inference perspectives. In both cases, model parameters are estimated from a sample of field plots and information from pixels corresponding to these units. In studies assisted by remote sensing, the shape of the plot used to fit regression models (typically circular) often differs from the shape of the population elements for prediction, where the area of interest is divided into equal tessellated parts. This raises interest in understanding the effect of the sampling unit shape on the mean of variables in forest stands of interest. Therefore, the objective of this study was to evaluate the effect of circular and square subplots, concentrically overlapped and arranged in an inverted Y cluster structure, over tree density, basal area, and aboveground biomass in a managed temperate forest in central Mexico. We used a Multivariate Generalised Linear Mixed Model, which considers the Gamma distribution of the variables and accounts for spatial correlation between Secondary Sampling Units nested within the Primary Sampling Unit. The main findings of this study indicate that the type of secondary sampling unit of the same area and centroid, whether circular or square, does not significantly affect the mean tree density (trees), basal area (m2), and aerial biomass. [ABSTRACT FROM AUTHOR]

Using multi-view images of forest plots to reconstruct dense point clouds and extract individual tree parameters enables rapid, high-precision, and cost-effective forest plot surveys. However, images captured at close range face challenges in forest reconstruction, such as unclear canopy reconstruction, prolonged reconstruction times, insufficient accuracy, and issues with tree duplication. To address these challenges, this paper introduces a new image dataset creation process that enhances both the efficiency and quality of image acquisition. Additionally, a block-matching-based multi-view reconstruction algorithm, Forest Multi-View Reconstruction with Enhanced Confidence-Guided Dynamic Domain Propagation (CDP-MVS), is proposed. The CDP-MVS algorithm addresses the issue of canopy and sky mixing in reconstructed point clouds by segmenting the sky in the depth maps and setting its depth value to zero. Furthermore, the algorithm introduces a confidence calculation method that comprehensively evaluates multiple aspects. Moreover, CDP-MVS employs a decentralized dynamic domain propagation sampling strategy, guiding the propagation of the dynamic domain through newly defined confidence measures. Finally, this paper compares the reconstruction results and individual tree parameters of the CDP-MVS, ACMMP, and PatchMatchNet algorithms using self-collected data. Visualization results show that, compared to the other two algorithms, CDP-MVS produces the least sky noise in tree reconstructions, with the clearest and most detailed canopy branches and trunk sections. In terms of parameter metrics, CDP-MVS achieved 100% accuracy in reconstructing tree quantities across the four plots, effectively avoiding tree duplication. The accuracy of breast diameter extraction values of point clouds reconstructed by CDPMVS reached 96.27%, 90%, 90.64%, and 93.62%, respectively, in the four sample plots. The positional deviation of reconstructed trees, compared to ACMMP, was reduced by 0.37 m, 0.07 m, 0.18 m and 0.33 m, with the average distance deviation across the four plots converging within 0.25 m. In terms of reconstruction efficiency, CDP-MVS completed the reconstruction of the four plots in 1.8 to 3.1 h, reducing the average reconstruction time per plot by six minutes compared to ACMMP and by two to three times compared to PatchMatchNet. Finally, the differences in tree height accuracy among the point clouds reconstructed by the different algorithms were minimal. The experimental results demonstrate that CDP-MVS, as a multi-view reconstruction algorithm tailored for forest reconstruction, shows promising application potential and can provide valuable support for forestry surveys. [ABSTRACT FROM AUTHOR]

Forests play a critical role in the provision of ecosystem services, and understanding their compositions, especially tree species, is essential for effective ecosystem management and conservation. However, identifying tree species is challenging and time-consuming. Recently, unmanned aerial vehicles (UAVs) equipped with various sensors have emerged as a promising technology for species identification due to their relatively low cost and high spatial and temporal resolutions. Moreover, the advancement of various deep learning models makes remote sensing based species identification more a reality. However, three questions remain to be answered: first, which of the state-of-the-art models performs best for this task; second, which is the optimal season for tree species classification in a temperate forest; and third, whether a model trained in one season can be effectively transferred to another season. To address these questions, we focus on tree species classification by using five state-of-the-art deep learning models on UAV-based RGB images, and we explored the model transferability between seasons. Utilizing UAV images taken in the summer and fall, we captured 8799 crown images of eight species. We trained five models using summer and fall images and compared their performance on the same dataset. All models achieved high performances in species classification, with the best performance on summer images, with an average F1-score was 0.96. For the fall images, Vision Transformer (ViT), EfficientNetB0, and YOLOv5 achieved F1-scores greater than 0.9, outperforming both ResNet18 and DenseNet. On average, across the two seasons, ViT achieved the best accuracy. This study demonstrates the capability of deep learning models in forest inventory, particularly for tree species classification. While the choice of certain models may not significantly affect performance when using summer images, the advanced models prove to be a better choice for fall images. Given the limited transferability from one season to another, further research is required to overcome the challenge associated with transferability across seasons. [ABSTRACT FROM AUTHOR]

Estimating forest growing stock volume (GSV) is crucial for forest growth and resource management, as it reflects forest productivity. National measurements are laborious and costly; however, integrating satellite data such as optical, Synthetic Aperture Radar (SAR), and airborne laser scanning (ALS) with National Forest Inventory (NFI) data and machine learning (ML) methods has transformed forest management. In this study, random forest (RF), support vector regression (SVR), and Extreme Gradient Boosting (XGBoost) were used to predict GSV using Estonian NFI data, Sentinel-2 imagery, and ALS point cloud data. Four variable combinations were tested: CO1 (vegetation indices and LiDAR), CO2 (vegetation indices and individual band reflectance), CO3 (LiDAR and individual band reflectance), and CO4 (a combination of vegetation indices, individual band reflectance, and LiDAR). Across Estonia's geographical regions, RF consistently delivered the best performance. In the northwest (NW), the RF model achieved the best performance with the CO3 combination, having an R2 of 0.63 and an RMSE of 125.39 m3/plot. In the southwest (SW), the RF model also performed exceptionally well, achieving an R2 of 0.73 and an RMSE of 128.86 m3/plot with the CO4 variable combination. In the northeast (NE), the RF model outperformed other ML models, achieving an R2 of 0.64 and an RMSE of 133.77 m3/plot under the CO4 combination. Finally, in the southeast (SE) region, the best performance was achieved with the CO4 combination, yielding an R2 of 0.70 and an RMSE of 21,120.72 m3/plot. These results underscore RF's precision in predicting GSV across diverse environments, though refining variable selection and improving tree species data could further enhance accuracy. [ABSTRACT FROM AUTHOR]

The objective of this review is to conduct a critical analysis of the current literature pertaining to segmentation techniques and provide a methodical summary of their impact on forestry-related activities, emphasizing their applications using LiDAR and imagery data. This review covers the challenges, progress, and application of these strategies in ecological monitoring, forest inventory, and tree species classification. Through the process of synthesizing pivotal discoveries from multiple studies, this comprehensive analysis provides valuable perspectives on the present status of research and highlights prospective areas for further exploration. The primary topics addressed encompass the approach employed for executing the examination, the fundamental discoveries associated with semantic segmentation and instance segmentation in the domain of forestry, and the ramifications of these discoveries for the discipline. This review highlights the effectiveness of semantic and instance segmentation techniques in forestry applications, such as precise tree species identification and individual tree monitoring. However, challenges such as occlusions, overlapping branches, and varying data quality remain. Future research should focus on overcoming these obstacles to enhance the precision and applicability of these segmentation methodologies. [ABSTRACT FROM AUTHOR]

Single-tree segmentation on multispectral UAV images shows significant potential for effective forest management such as automating forest inventories or detecting damage and diseases when using an additional classifier. We propose an automated workflow for segmentation on high-resolution data and provide our trained models in a Toolbox for ArcGIS Pro on our GitHub repository for other researchers. The database used for this study consists of multispectral UAV data (RGB, NIR and red edge bands) of a forest area in Germany consisting of a mix of tree species consisting of five deciduous trees and three conifer tree species in the matured closed canopy stage at approximately 90 years. Information of NIR and Red Edge bands are evaluated for tree segmentation using different vegetation indices (VIs) in comparison to only using RGB information. We trained Faster R-CNN, Mask R-CNN, TensorMask and SAM in several experiments and evaluated model performance on different data combinations. All models with the exception of SAM show good performance on our test data with the Faster R-CNN model trained on the red and green bands and the Normalized Difference Red Edge Index (NDRE) achieving best results with an F1-Score of 83.5% and an Intersection over Union of 65.3% on highly detailed labels. All models are provided in our TreeSeg toolbox and allow the user to apply the pre-trained models on new data. [ABSTRACT FROM AUTHOR]

Mobile devices such as smartphones, tablets or similar devices are becoming increasingly important as measurement devices in forestry due to their advanced sensors, including RGB cameras and LiDAR systems. This review examines the current state of applications of mobile devices for measuring biometric characteristics of individual trees and presents technologies, applications, measurement accuracy and implementation barriers. Passive sensors, such as RGB cameras have proven their potential for 3D reconstruction and analysing point clouds that improve single tree-level information collection. Active sensors with LiDAR-equipped smartphones provide precise quantitative measurements but are limited by specific hardware requirements. The combination of passive and active sensing techniques has shown significant potential for comprehensive data collection. The methods of data collection, both physical and digital, significantly affect the accuracy and reproducibility of measurements. Applications such as ForestScanner and TRESTIMATM have automated the measurement of tree characteristics and simplified data collection. However, environmental conditions and sensor limitations pose a challenge. There are also computational obstacles, as many methods require significant post-processing. The review highlights the advances in mobile device-based forestry applications and emphasizes the need for standardized protocols and cross-device benchmarking. Future research should focus on developing robust algorithms and cost-effective solutions to improve measurement accuracy and accessibility. While mobile devices offer significant potential for forest surveying, overcoming the above-mentioned challenges is critical to optimizing their application in forest management and protection. [ABSTRACT FROM AUTHOR]

Climate change influences carbon sequestration by forests, either positively or negatively. In the Mediterranean region, this effect is predominantly negative, although it seems to be compensated by the elevation. This study aims to analyse the impact of climate change on carbon sequestration in five pine species—Pinus halepensis, P. nigra, P. pinaster, P. pinea, and P. sylvestris—growing across an altitudinal gradient from 573 to 1743 m a.s.l. in central Spain. Data from 300 forest inventory plots in ten forests were used to first calculate carbon sequestration in 2024. To estimate future carbon sequestration, the expected values of precipitation and temperature for 2100 were determined, based on regionalised climate scenarios for RCP4.5 and RCP8.5. Values from 13 meteorological stations located around the forests, at different elevations, were analysed, conducting a statistical analysis to determine whether variations were significant. A statistically significant variation was detected for temperature and precipitation changes only under the RCP8.5 scenario. Using temperature and precipitation data for 2024 and 2100, net potential productivity in both years was established, considering its variation ratio equivalent as equivalent to that of growth and carbon sequestration. An inflection point was detected in 2100 at 1646 m a.s.l., with a decrease in productivity below and an increase above that elevation. Results reflect a decline in carbon sequestration in all the species, ranging from 6% in P. sylvestris to 28% in P. halepensis, conditioned by the elevation. Regionally, the average decrease would be 16.4%. In temperate and boreal regions, forest growth is expected to increase due to climate change, but the Mediterranean region will experience a significant decrease, except in mountain areas. To maintain current levels of carbon sequestration, it would be necessary to increase the existing carbon sinks through new plantations and the restoration of degraded forests. [ABSTRACT FROM AUTHOR]

Research related to Cerradão vegetation focuses more on the floristic-structural aspect, with rare studies on the quantification of volume and biomass stocks, and even fewer investigating the increments of these attributes. Using a systematic sampling method with subdivided strips and 400 m2 plots, the density found was 1135, 1165, and 1229 trees/ha in 2012, 2020, and 2023, respectively, in Lajeado State Park, Tocantins State, Brazil. Volume was estimated using the equation v = 0.000085 D 2.122270 H 0.666217 , and biomass was estimated using the equation A G B = 0.0673 ρ D 2 H 0.976 . Vegetation dynamics were assessed using growth increment, recruitment, mortality, turnover rate, and time. The results indicated that dynamics have increased since the start of monitoring. Typical Cerrado species, in the strict sense, were replaced by those from forest environments. The total production in volume and biomass was 160.91 m3/ha and 118.10 Mg/ha, respectively, in 2023. The species of Emmotum nitens, Mezilaurus itauba, Ocotea canaliculata, and Sacoglottis guianensis showed the highest increment values in volume and biomass. For the community, the average values were 4.04 m3/ha/year and 3.54 Mg/ha/year. The community has not yet reached its carrying capacity and stores a significant amount of biomass. This is influenced by the transition of the study area from an exploited environment to a conservation unit (park) and by its location in a transitional area with the Amazon biome. [ABSTRACT FROM AUTHOR]

The Cerrado has high plant and vertebrate diversity and is an important biome for conserving species and provisioning ecosystem services. Volume equations in this biome are scarce because of their size and physiognomic diversity. This study was conducted to develop specific volumetric models for the phytophysiognomies Gallery Forest, Dry Forest, Forest Savannah, and Savannah Woodland, a generic model and a model for Cerrado forest formation. Twelve 10 m × 10 m (100 m²) (National Forest Inventory) plots were used for each phytophysiognomy at different sites (regions) of the Federal District (FD) where trees had a diameter at breast height (DBH; 1.30 m) ≥5 cm in forest formations and a diameter at base height (Db; 0.30 m) ≥5 cm in savanna formations. Their diameters and heights were measured, they were cut and cubed, and the volume of each tree was obtained according to the Smalian methodology. Linear and nonlinear models were adjusted. Criteria for the selection of models were determined using correlation coefficients, the standard error of the estimates, and a graphical analysis of the residues. They were later validated by the chi-square test. The resultant models indicated that fit by specific phytophysiognomy was ideal; however, the generic and forest formation models exhibited similar performance to specific models and could be used in extensive areas of the Cerrado, where they represent a high potential for generalization. To further increase our understanding, similar research is recommended for the development of specific and generic models of the total volume in Cerrado areas. [ABSTRACT FROM AUTHOR]

White oak mortality is a significant concern in forest ecosystems due to its impact on biodiversity and ecosystem functions. Understanding the factors influencing white oak mortality is crucial for effective forest management and conservation efforts. In this study, we aimed to investigate the spatial pattern of WOM rates across the eastern US and explore the underlying processes behind the observed spatial patterns. Multicycle forest inventory and analysis data were compiled to capture all white oak plots. WOM data were selected across plot systems that utilized declining basal areas between two periods. Ripley's K function was used to study the spatial pattern of WOM rates. Results showed clustered patterns of WOM rates at local and broad scales that may indicate stand-level competition and regional variables affecting white oaks' dynamics across southern and northern regions. Results also indicated random patterns at broad scales, suggesting variations in topographic and hydrological conditions across the south and northern regions. However, the central region indicated both clustered and random patterns at the local scale that might be associated with inter-species competition and the possibility of environmental heterogeneity, respectively. Furthermore, uniform patterns of WOM rate at a broad scale across all regions might suggest regions with spatially homogeneous environmental factors acting on the dynamics of white oaks. This research might be helpful in identifying impacted areas of white oaks at varying scales. Future research is needed to comprehensively assess biotic and abiotic factors at various spatial scales aimed at mitigating WOM. [ABSTRACT FROM AUTHOR]

Tree diameter at breast height (DBH) can visually reflect the size of trees and is closely related to forest carbon sinks. As its continuous change is affected by the growing environment, it is an important indicator for forest surveys, and it is of great value for forest carbon economy and microecological research. In order to realize the accurate and continuous digital measurement of tree diameter at breast height, this paper develops a low-power tree diameter at breast height continuous measurement device based on the magneto-resistive effect. Compared to the traditional method of tree diameter measurement, this device has the advantages of real-time transmission of data, low-cost, anti-interference, and so on. In Zhejiang Jinhua Nanshan Nature Reserve, at a number of sample sites, tree diameter at breast height for 10 different species of trees was measured every 16 h before developing the corresponding upper software, background analysis software, and real-time acceptance of the measurement data to make timely analysis. After 12 months, experimental application and data analysis showed that the measurement accuracy of this device can be up to 0.001 mm. Compared to traditional tree diameter ruler measurement, measurement error is within 0.1%. This device, therefore, enables the continuous measurement, transmission, storage, and analysis of the tree diameter as a whole, and reveals the growth and carbon sink change rules of the tree diameter of a specific region at a certain age. [ABSTRACT FROM AUTHOR]

Yield data represent a valuable layer for supporting decision-making as they reflect crop management results. Forestry decision-makers often rely on coarse spatial resolution data (e.g., forest inventory plots) despite the availability of modern harvesters that can provide high-resolution forestry yield data. The objectives of this study were to present a method for generating high-resolution Eucalyptus grandis yield data (individual tree-level) and explore their applications, such as correlation analysis with soil attributes to aid nutrient recommendations. Two evaluations were conducted at two sites in Brazil: (a) assessing the positioning accuracy of the global navigation satellite system (GNSS) receiver positioning, and (b) analyzing the yield data and their correlation with the soil attributes. The results indicated that positioning the GNSS receiver at the harvesting head provided higher accuracy than placement at the top of the harvester cabin for individual tree-level data. Reliable yield data were generated despite the GNSS receiver's increased susceptibility to damage when mounted on a harvest head. The linear correlation analysis between the Eucalyptus grandis yield data and soil attributes showed both negative (Clay, B, S, coarse sand, and potential acidity − H + Al) and positive correlations (K, Mg, pH-SMP, Ca, sum of bases, pH, base saturation, fine sand, total sand, and silt content). This study demonstrates the feasibility of obtaining high-resolution yield data at the individual tree-level and their correlation with soil attributes, providing valuable insights for improving forestry decision-making. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Tropical forests play an important role in mitigating global climate change, emphasizing the need for reliable estimates of forest carbon stocks at regional and global scales. This is essential for effective carbon management, which involves strategies like emission reduction and enhanced carbon sequestration through forest restoration and conservation. However, reliable sample-based estimations of forest carbon stocks require accurate allometric equations, which are lacking for the rainforests of the Atlantic Forest Domain (AFD). In this study, we fitted biomass equations for the three main AFD forest types and accurately estimated the amount of carbon stored in their above-ground biomass (AGB) in Rio de Janeiro state, Brazil. Using non-destructive methods, we measured the total wood volume and wood density of 172 trees from the most abundant species in the main remnants of rainforest, semideciduous forest, and restinga forest in the state. The biomass and carbon stocks were estimated with tree-level data from 185 plots obtained in the National Forest Inventory conducted in Rio de Janeiro. Our locally developed allometric equations estimated the state's biomass stocks at 70.8 ± 5.4 Mg ha−1 and carbon stocks at 35.4 ± 2.7 Mg ha−1. Notably, our estimates were more accurate than those obtained using a widely applied pantropical allometric equation from the literature, which tended to overestimate biomass and carbon stocks. These findings can be used for establishing a baseline for monitoring carbon stocks in the Atlantic Forest, especially in the context of the growing voluntary carbon market, which demands more consistent and accurate carbon stock estimations. [ABSTRACT FROM AUTHOR]

Forests are an important part of natural resources and play an important role in carbon sinks. We measured carbon sinks in provincial forest parks using data from four forest inventory surveys in China and the forest stock expansion method. Carbon emissions from forest tourism were also estimated using energy statistics and forest park tourism data. On this basis, spatial analysis was used to summarize the spatial and temporal evolution of the carbon balance and the analysis of influencing factors. The results show the following: (1) With the passage of time, the carbon emissions from forest tourism in all provinces have increased to different degrees, and the national forest tourism carbon emissions have increased from 1,071,390.231 (million tons) in 2003 to 286,255,829.7 (million tons) in 2018; spatially, the distribution of carbon emissions from forest tourism is uneven, with an overall high in the south and low in the north, and a high in the east and a low in the west. (2) The carbon sink of forest parks showed a trend of gradual growth and spatially formed a spatial pattern of high in the northeast and low in the southwest, which is consistent with the distribution of forest resources in China. (3) For forest tourism carbon emissions, the total number of tourists, tourism income, and playing roads are significant influencing factors, and the baseline regression coefficients are 0.595, 0.433, and 0.799, respectively, while for forest park carbon sinks, the number of forest park employees can play a certain positive role in carbon sinks, with the regression coefficient being 1.533. [ABSTRACT FROM AUTHOR]

In remote sensing image processing, when categorizing images from multiple remote sensing data sources, the deepening of the network hierarchy is prone to the problems of feature dispersion, as well as the loss of semantic information. In order to solve this problem, this paper proposes to integrate a parallel network architecture HDAM-Net algorithm with a hybrid dual attention mechanism Hybrid dual attention mechanism for forest land cover change. Firstly, we propose a fusion MCA + SAM (MS) attention mechanism to improve VIT network, which can capture the correlation information between features; secondly, we propose a multilayer residual cascade convolution (MSCRC) network model using Double Cross-Attention Module (DCAM) attention mechanism, which is able to efficiently utilize the spatial dependency between multiscale encoder features: the spatial dependency between multiscale encoder features. Finally, the dual-channel parallel architecture is utilized to solve the structural differences and realize the enhancement of forestry image classification differentiation and effective monitoring of forest cover changes. In order to compare the performance of HDAM-Net, mountain urban forest types are classified based on multiple remote sensing data sources, and the performance of the model is evaluated. The experimental results show that the overall accuracy of the algorithm proposed in this paper is 99.42%, while the Transformer (ViT) is 96.92%, which indicates that the proposed classifier is able to accurately determine the cover type.The HDAM-Net model emphasizes the effectiveness in terms of accurately classifying the land, as well as the forest types by using multiple remote sensing data sources for predicting the future trend of the forest ecosystem. In addition, the land utilization rate and land cover change can clearly show the forest cover change and support the data to predict the future trend of the forest ecosystem so that the forest resource survey can effectively monitor deforestation and evaluate forest restoration projects. [ABSTRACT FROM AUTHOR]

Moso bamboo (Phyllostachys edulis (Carrière) J. Houzeau), which is native to China, is considered to be an invasive species due to its powerful asexual reproductive capabilities that allow it to rapidly spread into neighboring ecosystems and replace existing plant communities. In the absence of human intervention, it remains poorly understood how indigenous moso bamboo forests naturally expand into surrounding areas over the long term, and whether these patterns vary with environmental changes. Using multi-year forest resource inventory data, we extracted moso bamboo patches that emerged from 2010 to 2020 and proposed a bamboo expansion index to calculate the average rate of patch expansion during this period. Using the first global 30 m land-cover dynamic monitoring product with a fine classification system, we assessed the expansion speeds of moso bamboo into various areas, particularly forests with different canopy closures and categories. Using parameter-optimized geographic detectors, we explored the significance of multi-factors in the expansion process. The results indicate that the average expansion rate of moso bamboo forests in China is 1.36 m/y, with evergreen broadleaved forests being the primary area for invasion. Moso bamboo expands faster into open forest types (0.15 < canopy closure < 0.4), shrublands, and grasslands. The importance of factors influencing the expansion rate is ranked as follows: temperature > chemical properties of soil > light > physical properties of soil > moisture > atmosphere > terrain. When considering interactions, the primary factors contributing to expansion rates include various climate factors and the combined effect of climate factors and soil factors. Our work underscores the importance of improving the quality and density of native vegetation, such as evergreen broadleaved forests. Effective management strategies, including systematic monitoring of environmental variables, as well as targeted interventions like bamboo removal and soil moisture control, are essential for mitigating the invasion of moso bamboo. [ABSTRACT FROM AUTHOR]

The standardized methods used in carbon markets require measurement of the biomass and carbon stored in trees, which can be quantified through allometric equations. The objective of this study was to analyze aboveground biomass estimates with allometric models in three mangrove species and compare them with those used by the Climate Action Reserve (CAR) standard. The mangrove forest in Tabasco, Mexico, was certified with the Forest Protocol for Mexico Version 2.0 (FPM) of the CAR standard. Allometric equations for mangrove species were reviewed to determine the most suitable equation for the calculation of biomass. The predictions of the allometric equations of the FPM were analyzed with data from Tabasco from the National Forest and Soil Inventory 2015–2020, and the percentages of trees within the ranges of diameters of the FPM equations were determined. The FPM equations generated higher biomass values for Rhizophora mangle and lower values for Avicennia germinans than the seven equations with which they were compared. In the mangrove swamp of Ejido Úrsulo Galván, Tabasco, 81.8% of the biomass of A. germinans, 34.4% of Laguncularia racemosa and 24.0% of R. mangle were within the diameter range of the FPM equations, and in Tabasco, 28.5% of A. germinans, 16.7% of L. racemosa and 5.7% of R. mangle were within the diameter range. For A. germinans and R. mangle, we recommend using the equation that considers greater maximum diameters. The allometric equations of the FPM do not adequately predict a large percentage of the biomass. [ABSTRACT FROM AUTHOR]

Accurately assessing forest structure and maintaining up-to-date information about forest structure is crucial for various forest planning efforts, including the development of reliable forest plans and assessments of the sustainable management of natural resources. Field measurements traditionally applied to acquire forest inventory information (e.g., basal area, tree volume, and aboveground biomass) are labor intensive and time consuming. To address this limitation, remote sensing technology has been widely applied in modeling efforts to help estimate forest inventory information. Among various remotely sensed data, LiDAR can potentially help describe forest structure. This study was conducted to estimate and map forest inventory information across the Shoal Creek and Talladega Ranger Districts of the Talladega National Forest by employing ALS-derived data and aerial photography. The quality of the predictive models was evaluated to determine whether additional remotely sensed data can help improve forest structure estimates. Additionally, the quality of general predictive models was compared to that of species group models. This study confirms that quality level 2 LiDAR data were sufficient for developing adequate predictive models (R2adj. ranging between 0.71 and 0.82), when compared to the predictive models based on LiDAR and aerial imagery. Additionally, this study suggests that species group predictive models were of higher quality than general predictive models. Lastly, landscape level maps were created from the predictive models and these may be helpful to planners, forest managers, and landowners in their management efforts. [ABSTRACT FROM AUTHOR]

The accurate large-scale classification of tree species is crucial for the monitoring, protection, and management of the Earth's invaluable forest ecosystems. Numerous previous studies have recognized the suitability of satellite imagery, particularly Sentinel-2 imagery, for this task. In this study, we utilized a dense phenology Sentinel-2 time series, which offered consistent data across multiple granules, to map tree species across the entire forested area in Austria. Aiming for the classification scheme to more accurately represent actual forest conditions, we included mixed tree species and sparsely populated classes (classes with sparse canopy cover) alongside pure tree species classes. To enhance the training data for the mixed and sparse classes, synthetic data creation was employed. Autocorrelation has significant implications for the validation of thematic maps. To investigate the impact of spatial dependency on validation data, two methods were employed at numerous split and buffer distances: spatial split validation and a validation method based on a buffered ground reference probability samples provided by the National Forest inventory (NFI). While a random training data holdout set yielded 99% accuracy, the spatial split validation resulted in 74% accuracy, emphasizing the importance of accounting for spatial autocorrelation when validating with holdout sets derived from polygon-based training data. The validation based on NFI data resulted in 55% overall accuracy, 91% post-hoc pure class accuracy, and 79% accuracy when confusions in phenological proximity were disregarded (e.g., spruce–larch confused with spruce). The significant differences in accuracy observed between spatial split and NFI validation underscore the challenge for polygon-based training data to capture ground reference forest complexity, particularly in areas with diverse forests. This hardship is further accentuated by the pure class accuracy of 91%, revealing the substantial impact of mixed stands on the accuracy of tree species maps. [ABSTRACT FROM AUTHOR]

Stakeholder involvement can foster more socially and environmentally sustainable management of natural resources, including forests. However, few studies have approached the effect of knowledge on stakeholders' involvement in forest management. This study intends to contribute to filling this gap by exploring the relationship between access to knowledge, involvement, stakeholders' profiles, and levels of influence and interest regarding public forest management strategies. To this end, this article examines the data collected through a questionnaire directed to all the stakeholders potentially interested in the management of the Matas do Litoral. Matas do Litoral are part of the 3% of publicly managed forests in Portugal. The results reveal a discrepancy between the high levels of interest regarding Matas do Litoral management, and low levels of influence on those processes. Most of the stakeholders surveyed know forest management strategies, and their involvement in those strategies is limited. The proximity and role of governmental organizations are key factors underlying knowledge levels among the various stakeholders. Furthermore, knowledge acts as a critical factor in encouraging the stakeholders' influence and involvement in management strategies and policies. This study gives insights regarding the need for knowledge management as a tool for empowering local stakeholders and promoting their involvement in bottom-up forest management strategies. [ABSTRACT FROM AUTHOR]

Low-altitude aerial photogrammetry can be an alternative source of forest inventory data and a practical tool for rapid forest attribute updates. The availability of low-cost unmanned aerial systems (UASs) and continuous technological advances in terms of their flight duration and automation capabilities makes these solutions interesting tools for supporting various forest management needs. However, any practical application requires a priori empirical validation and optimization steps, especially if it is to be used under different forest conditions. This study investigates the influence of the main flight parameters, i.e., ground sampling distance and photo overlap, on the performance of individual tree detection (ITD) stand-level forest inventories, based on photogrammetric data obtained from budget unmanned aerial systems. The investigated sites represented the most common forest conditions in the Polish lowlands. The results showed no direct influence of the investigated factors on growing stock volume predictions within the analyzed range, i.e., overlap from 80 × 80 to 90 × 90% and GSD from 2 to 6 cm. However, we found that the tree detection ratio had an influence on estimation errors, which ranged from 0.6 to 15.3%. The estimates were generally coherent across repeated flights and were not susceptible to the weather conditions encountered. The study demonstrates the suitability of the ITD method for small-area forest inventories using photogrammetric UAV data, as well as its potential optimization for larger-scale surveys. [ABSTRACT FROM AUTHOR]

The role of forests in providing multiple goods and services has been recognized worldwide. In such a context, reliable spatial predictions of forest attributes such as tree volume and current increment are fundamental for conducting forest monitoring, improving restoration programs, and supporting decision-making processes. This article presents the methodology and the results of the wall-to-wall spatialization of the growing stock volume and the current annual increment measured in 273 plots of data of the Italian National Forest Inventory over an area of more than 3260 km2 in the Friuli Venezia Giulia region (Northeast Italy). To this aim, a random forest model was tested using as predictors 4 spectral indices from Sentinel-2, a high-resolution Canopy Height Model derived from LiDAR, and geo-morphological data. According to the Leave One Out cross-validation procedure, the model for the growing stock shows an R2 and an RMSE% of 0.67 and 41%, respectively. Instead, an R2 of 0.47 and an RMSE% of 57% were obtained for the current annual increment. The validation with an independent dataset further improved the models' performances, yielding significantly higher R2 values of 0.84 and 0.83 for volume and for increment, respectively. Our results underline a relatively higher importance of LiDAR-derived metrics compared to other covariates in estimating both attributes, as they were even twice as important as vegetation indices for growing stock. Therefore, these metrics are promising for the development of a national LiDAR-based model. [ABSTRACT FROM AUTHOR]

As the most important way to mitigate climate change, forest carbon storage has been the subject of extensive research. A comprehensive study was carried out to investigate the influence of elevation gradients and diameter classes on the forest growth, composition, diversity, and carbon pools of the Bagh Drush Khel Forest area. Research revealed that elevation gradients significantly influenced the composition, diversity, and carbon pools in forests. At lower elevations, Eucalyptus camaldulensis was the dominant species, with Olea ferruginea as a co-dominant species, whereas at higher elevations, Pinus roxburghii was the dominant species with Quercus incana as a co-dominant species. Regeneration was higher at higher elevations with the maximum number of saplings and seedlings of P. roxburghii. Species diversity association with elevation was negative (R2 = −0.44; p < 0.05—Shannon Index). Soil organic carbon (SOC association with elevation was non-significant while positive with DBH classes (R2 = 0.37; p < 0.05). Overall, carbon pool association with elevation and diameter at breast height (DBH) were negative (R2 = −0.73; p < 0.05) and (R2 = −0.45; p < 0.05). Litter biomass correlated positively with elevation (R2 = 0.25; p < 0.05) and DBH (R2 = 0.11; p < 0.05), while deadwood biomass correlated negatively with elevation gradients (R2 = −0.25; p < 0.05), and no effect was observed for DBH classes. The highest carbon stock (845.89 t C/ha) was calculated at low elevations, which decreased to (516.27 t C/ha) at high elevations. The overall carbon stock calculated was (2016.41 t C/ha) respectively. A total of six tree species were found at the study site. Future research is essential for forest health monitoring and understanding fine-scale impacts. This study offers a methodological framework for similar investigations in unexplored yet potentially significant forest regions worldwide. [ABSTRACT FROM AUTHOR]

In the context of global climate change, eucalyptus stands in the planted forest sector have become a viable alternative for reducing greenhouse gas (GHG) emissions, in addition to presenting great potential for the carbon (C) stock. Thus, the objective of this study was to quantify C stocks in different eucalyptus compartments, in addition to evaluating three mathematical models at the individual tree level. We evaluated four areas of eucalyptus stands located in the Federal District, Brazil. The data were collected from the forest inventory and rigorous cubing procedures using the following statistical models: Spurr, Schumacher–Hall, and adapted Schumacher–Hall. The highest Pearson's linear modification coefficient, lowest root means square error percentage (RMSE%), and lowest Akaike information criterion (AIC) were used to select the best model. The C content and stock varied between the compartments and areas studied owing to age and, above all, genetic differences. Clone I224 had the highest carbon concentration per acre at 233.35 Mg ha−1 and carbon difference per compartment. The adapted Schumacher–Hall was the best model. It included data on biometric factors, such as the diameter at breast height, height, and age. The contribution of eucalyptus plantations to carbon sequestration is fundamental to socioenvironmental enhancement. [ABSTRACT FROM AUTHOR]