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Abstract: Forests are a large carbon sink and could serve as natural climate solutions that help moderate future warming. Thus, establishing forest carbon baselines is essential for tracking climate‐mitigation targets. Western US forests are natural climate solution hotspots but are profoundly threatened by drought and altered disturbance regimes. How these factors shape spatial patterns of carbon storage and carbon change over time is poorly resolved. Here, we estimate live and dead forest carbon density in 19 forested western US ecoregions with national inventory data (2005–2019) to determine: (a) current carbon distributions, (b) underpinning drivers, and (c) recent trends. Potential drivers of current carbon included harvest, wildfire, insect and disease, topography, and climate. Using random forests, we evaluated driver importance and relationships with current live and dead carbon within ecoregions. We assessed trends using linear models. Pacific Northwest (PNW) and Southwest (SW) ecoregions were most and least carbon dense, respectively. Climate was an important carbon driver in the SW and Lower Rockies. Fire reduced live and increased dead carbon, and was most important in the Upper Rockies and California. No ecoregion was unaffected by fire. Harvest and private ownership reduced carbon, particularly in the PNW. Since 2005, live carbon declined across much of the western US, likely from drought and fire. Carbon has increased in PNW ecoregions, likely recovering from past harvest, but recent record fire years may alter trajectories. Our results provide insight into western US forest carbon function and future vulnerabilities, which is vital for effective climate change mitigation strategies.

Errors in forest inventory data can lead to sub-optimal management decisions and dramatic economic losses. Forest inventory approaches are typically evaluated by their levels of precision and accuracy; however, this overlooks the specific usefulness of the data in decision-making. By evaluating the value of information (VoI), we can assess the usefulness of the data for specific decision-making problems. We evaluated the VoI through stochastic programming for four airborne laser scanning-based inventory approaches. The stochastic programming model explored the trade-off between the maximal net present value and the minimal conditional value at risk of meeting specified periodic income targets. We evaluated a range of periodic targets and risk aversion preference levels. To compare the performance of the inventory approaches, we used a reference dataset that was acquired using a forest harvester with precise positioning. For a wide range of the trade-offs, inventory approaches with higher-quality information provided the best overall performance. If only one of the extreme objectives was desired, less precise inventory approaches were sufficient to produce high-quality solutions. [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]

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]

Since it is impossible in practice to create a sampling frame for the population of forest trees, forest inventories have relied on indirect sampling methods. This indirect sampling uses two populations: the discrete populations of trees and the continuous population of points, from which trees are being sampled. Important works such as Mandallaz, Eriksson, and Stevens and Urquhart brought the fundamental elements in the formalization of the sampling of trees, by defining the duality principle that relates both populations. They led to the so-called continuous population approach where trees attributes are transformed into attribute density values. However, in these approaches, the trees quickly fade away despite being the target population, while their weight is calculated as the inverse of their inclusion probability. We explain how the generalized weight share method (GWSM) can be used to formalize the link between the two populations. GWSM allows to revisit previous concepts proposed to solve the question of how to produce estimations from tree-level attributes, under uniform random or more complex sampling designs. The principles of the method are explained, and its functioning is illustrated under a variety of points and trees sampling designs, including fixed-area, Bitterlich, and cluster sampling. [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]

Personal laser scanning (PLS) systems are gaining popularity in forest inventory research and practice. They are primarily utilized on circular or compact rectangular sample plots to mitigate potential instrument drift and enhance tree detection rates, and a closed-loop scan path is usually implemented to achieve these objectives, ensuring thorough coverage of the plot. This study introduced a novel approach by applying the distance-sampling framework to PLS data collected during walks along line transects. Modeling the distance-dependent probability of tree detection using PLS coupled with automatic routines for point cloud processing aimed to ascertain the optimal width of line-plots to maximize tree detection rates. The optimized plots exhibited tree detection rates exceeding 99%, which facilitated accurate estimates of tree density, basal area, and growing stock volumes. This proposed method demonstrated considerable potential for data collection while walking along line transects in forests. For instance, the otherwise unproductive working time of field crews moving between systematically arranged sample plots can be utilized for additional data collection without generating additional costs. This innovative approach not only enhances operational efficiency but also establishes a foundation for further advancements to explore PLS applications in forest management practices.

Abstract Key message Data acquisition of remote sensing products is an essential component of modern forest inventories. The quality and properties of optical remote sensing data are further emphasised in tree species-specific inventories, where the discrimination of different tree species is based on differences in their spectral properties. Furthermore, phenology affects the spectral properties of both evergreen and deciduous trees through seasons. These confounding factors in both sensor configuration and timing of data acquisition can result in unexpectedly complicated situations if not taken into consideration. This paper examines how the timing of data acquisition and sensor properties influence the prediction of tree species proportions and volumes in a boreal forest area dominated by Norway spruce and Scots pine, with a smaller presence of deciduous trees. Context The effectiveness of remote sensing for vegetation mapping depends on the properties of the survey area, mapping objectives and sensor configuration. Aims The objective of this study was to investigate the plot-level relationship between seasonality and different optical band configurations and prediction performance of common boreal tree species. The study was conducted on a 40-ha study area with a systematically sampled circular field plots. Methods Tree species proportions (0–1) and volumes (m3 ha−1) were predicted with repeated remote sensing data collections in three stages of the growing season: prior (spring), during (summer) and end (autumn). Sensor band configurations included conventional RGB and multispectral (MS). The importance of different wavelengths (red, green, blue, near-infrared and red-edge) and predictive performance of the different band configurations were analysed using zero–one-inflated beta regression and Gaussian process regression. Results Prediction errors of broadleaves were most affected by band configuration, MS data resulting in lower prediction errors in all seasons. The MS data exhibited slightly lower prediction errors with summer data acquisition compared to other seasons, whereas this period was found to be less suitable for RGB data. Conclusion The MS data was found to be much less affected by seasonality than the RGB data. Spring was found to be the least optimal season to collect MS and RGB data for tree species-specific predictions.

Key message: Data acquisition of remote sensing products is an essential component of modern forest inventories. The quality and properties of optical remote sensing data are further emphasised in tree species-specific inventories, where the discrimination of different tree species is based on differences in their spectral properties. Furthermore, phenology affects the spectral properties of both evergreen and deciduous trees through seasons. These confounding factors in both sensor configuration and timing of data acquisition can result in unexpectedly complicated situations if not taken into consideration. This paper examines how the timing of data acquisition and sensor properties influence the prediction of tree species proportions and volumes in a boreal forest area dominated by Norway spruce and Scots pine, with a smaller presence of deciduous trees. Context: The effectiveness of remote sensing for vegetation mapping depends on the properties of the survey area, mapping objectives and sensor configuration. Aims: The objective of this study was to investigate the plot-level relationship between seasonality and different optical band configurations and prediction performance of common boreal tree species. The study was conducted on a 40-ha study area with a systematically sampled circular field plots. Methods: Tree species proportions (0–1) and volumes (m3 ha−1) were predicted with repeated remote sensing data collections in three stages of the growing season: prior (spring), during (summer) and end (autumn). Sensor band configurations included conventional RGB and multispectral (MS). The importance of different wavelengths (red, green, blue, near-infrared and red-edge) and predictive performance of the different band configurations were analysed using zero–one-inflated beta regression and Gaussian process regression. Results: Prediction errors of broadleaves were most affected by band configuration, MS data resulting in lower prediction errors in all seasons. The MS data exhibited slightly lower prediction errors with summer data acquisition compared to other seasons, whereas this period was found to be less suitable for RGB data. Conclusion: The MS data was found to be much less affected by seasonality than the RGB data. Spring was found to be the least optimal season to collect MS and RGB data for tree species-specific predictions. [ABSTRACT FROM AUTHOR]

Introduction: The knowledge about forest growth, influenced by factors such as tree species, tree age, and environmental conditions, is a key for future forest preservation. Height and age data can be combined to describe forest growth and used to infer known environmental effects. Methods: In this study, we built 14 height growth curves for stands composed of monospecific or mixed species using ground measurements and satellite data. We built a random forest height model from tree species, age, area of disturbance, and 125 environmental parameters (climate, altitude, soil composition, geology, stand ownership, and proximity to road and urban areas). Using feature elimination and SHapley Additive exPlanations (SHAP) analysis, we identified six key features explaining the forest growth and investigated how they affect the height. Results: The agreement between satellite and ground data justifies their simultaneous exploitation. Age and tree species are the main predictors of tree height (49% and 10%, respectively). The disturbed patch area, revealing the regeneration method, impacts post-disturbance growth at 19%. The soil pH, altitude, and climatic water budget in summer impact tree height differently depending on the age and tree species. Discussion: Methods integrating satellite and field data show promise for analyzing future forest evolution. [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]

The Andean Patagonian forests are characterized, on the one hand, by a high degree of naturalness and, on the other hand, by signs of degradation by grazing, unsustainable timber use, the invasion of non-native tree species and anthropogenic fire. Considering the large extent of these forests and the variety of uses, traditional forestry practices should be revised in light of modern technologies. We employed Landsat time series from 1998 to 2020, accompanied by Landsat images from 1985, 1986, 1987, and 1992 to detect the fine-scale gap dynamics of Nothofagus pumilio forests in central Patagonia, Argentina. These gaps can occur naturally (i.e. death of old trees) or can be anthropogenically induced (i.e. after logging). In total, 41 permanently established study plots covering an area of 40 ha were investigated. Our aim was to test the viability of the TimeSync method for detecting fine-scale disturbances. This method was proposed in 2010, and up to date, it has only been employed to study vegetation disturbance of a scale that affects several pixels. Our adaptation of the TimeSync method proved to be useful in identifying the time and intensity of changes in tree structure at the pixel scale. Accordingly, it can support forest monitoring and management by providing a cost-effective proxy for natural dynamics. [ABSTRACT FROM AUTHOR]

Tree allometric models, essential for monitoring and predicting terrestrial carbon stocks, are traditionally built on global databases with forest inventory measurements of stem diameter (D) and tree height (H). However, these databases often combine H measurements obtained through various measurement methods, each with distinct error patterns, affecting the resulting H:D allometries. In recent decades, terrestrial laser scanning (TLS) has emerged as a widely accepted method for accurate, non‐destructive tree structural measurements. This study used TLS data to evaluate the prediction accuracy of forest inventory‐based H:D allometries and to develop more accurate pantropical allometries. We considered 19 tropical rainforest plots across four continents. Eleven plots had forest inventory and RIEGL VZ‐400(i) TLS‐based D and H data, allowing accuracy assessment of local forest inventory‐based H:D allometries. Additionally, TLS‐based data from 1951 trees from all 19 plots were used to create new pantropical H:D allometries for tropical rainforests. Our findings reveal that in most plots, forest inventory‐based H:D allometries underestimated H compared with TLS‐based allometries. For 30‐metre‐tall trees, these underestimations varied from −1.6 m (−5.3%) to −7.5 m (−25.4%). In the Malaysian plot with trees reaching up to 77 m in height, the underestimation was as much as −31.7 m (−41.3%). We propose a TLS‐based pantropical H:D allometry, incorporating maximum climatological water deficit for site effects, with a mean uncertainty of 19.1% and a mean bias of −4.8%. While the mean uncertainty is roughly 2.3% greater than that of the Chave2014 model, this model demonstrates more consistent uncertainties across tree size and delivers less biased estimates of H (with a reduction of 8.23%). In summary, recognizing the errors in H measurements from forest inventory methods is vital, as they can propagate into the allometries they inform. This study underscores the potential of TLS for accurate H and D measurements in tropical rainforests, essential for refining tree allometries. [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]

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]

This study proposes a low-cost method for crown segmentation and forest inventory based on satellite remote sensing images and the deep learning model BlendMask. Taking Beijing Jingyue ecoforestry as the experimental area, we combined the field survey data and satellite images, and constructed the dataset independently, for model training. The experimental results show that the F1-score of Sophora japonica, Pinus tabulaeformis, and Koelreuteria paniculata reached 87.4%, 85.7%, and 86.3%, respectively. Meanwhile, we tested for the study area with a total area of 146 ha, and 27,403 tree species were identified in nine categories, with a total crown projection area of 318,725 m2. We also fitted a biomass calculation model for oil pine (Pinus tabulaeformis) based on field measurements and assessed 205,199.69 kg of carbon for this species across the study area. Additionally, we compared the model to U-net, and the results showed that BlendMask has strong crown-segmentation capabilities. This study demonstrates that BlendMask can effectively perform crown segmentation and forest inventory in large-scale complex forest areas, showing its great potential for forest resource management. [ABSTRACT FROM AUTHOR]

Forest age is one of most important biological factors that determines the magnitude of vegetation carbon sequestration. A spatially explicit forest age dataset is crucial for forest carbon dynamics modeling at the regional scale. However, owing to the high spatial heterogeneity in forest age, accurate high-resolution forest age data are still lacking, which causes uncertainty in carbon sink potential prediction. In this study, we obtained a 1 km resolution forest map based on the fusion of multiscale age information, i.e., the ninth (2014–2018) forest inventory statistics of China, with high accuracy at the province scale, and a field-observed dataset covering 6779 sites, with high accuracy at the site scale. Specifically, we first constructed a random forest (RF) model based on field-observed data. Utilizing this model, we then generated a spatially explicit forest age map with a 1 km resolution (random forest age map, RF map) using remotely sensed data such as tree height, elevation, meteorology, and forest distribution. This was then used as the basis for downscaling the provincial-scale forest inventory statistics of the forest ages and retrieving constrained maps of forest age (forest inventory constrained age maps, FIC map), which exhibit high statistical accuracy at both the province scale and site scale. The main results included the following: (1) RF can be used to estimate the site-scale forest age accurately (R2 = 0.89) and has the potential to predict the spatial pattern of forest age. However, (2) owing to the impacts of sampling error (e.g., field-observed sites are usually located in areas exhibiting relatively favorable environmental conditions) and the spatial mismatch among different datasets, the regional-scale forest age predicted by the RF model could be overestimated by 71.6%. (3) The results of the downscaling of the inventory statistics indicate that the average age of forests in China is 35.1 years (standard deviation of 21.9 years), with high spatial heterogeneity. Specifically, forests are older in mountainous and hilly areas, such as northeast, southwest, and northwest China, than in southern China. The spatially explicit dataset of the forest age retrieved in this study encompasses synthesized multiscale forest age information and is valuable for the research community in assessing the carbon sink potential and modeling carbon dynamics. [ABSTRACT FROM AUTHOR]

Forest owners need simple and efficient tools to capture and understand forest metrics. Additionally, they could benefit from emerging technologies in forest analysis. Light detection and ranging (LiDAR) sensors allow for precise measurements of different variables and can be used more easily in forestry settings thanks to their introduction into smartphones and tablets. ForestScanner and Arboreal Forest are two applications (apps) that allow for the measurement of tree diameter at breast height (DBH) on LiDAR-equipped devices. Our study sought to analyze and compare (1) the timing of traditional and LiDAR-based forest inventory methods and (2) the accuracy of traditional and LiDAR-based forest measurements. We established a series of plots to record and compare tree DBH and measurement time between a traditional diameter tape, ForestScanner, and Arboreal Forest. We found that DBH measurements from the apps were in good agreement with diameter tape measurements; however, both apps tended to underestimate DBH. Additionally, measurement time for both apps was found to be significantly shorter than traditional tape measurements. Further improvements in LiDAR apps can present a simple and efficient way for future forest analysis by seasoned foresters and private landowners. Study Implications : LiDAR smartphone applications represent a cost-effective and time-efficient method for wide-spread forest management, especially among landowners who have little access to forestry-specific equipment. As these technologies continue to advance, the incorporation of functions for plot establishment, height measurements, biomass estimations, and ecosystem dynamics could become valuable assets in the forest industry. We encourage individuals to further test LiDAR measurement applications themselves, as further training on these applications will help developers to improve application accuracy. [ABSTRACT FROM AUTHOR]

Sampling surveys are broadly employed in forest inventories due to their efficiency in quantifying forest characteristics. To this end, selecting an appropriate sampling met- hod is essential. This study introduces a new application of horizontal point sampling (HPS). We assess the statistical properties of HPS with variations in crown basal area factor (CBAF) and sample sizes (n). The study further conducts a comparative analysis between HPS and fixed-radius (FR) plot sampling methods. The study was done in an actual open coppice forest and in a simulated forest, using sampling simulations with a large number of repetitions. In HPS, a greater CBAF for a given sample size lead to a higher relative standard error (RSE %). Multiple regression model showed that both n and CBAF influence RSE %. There was an inverse relationship between n and RSE%, whereas a positive relationship was found between CBAF and RSE %. A combination of HPS and crown relascope is easily applicable in forest inventories. This combined method demonstrates efficiency, when compared to the traditional FR for estimating crown cover area in open coppice forests. [ABSTRACT FROM AUTHOR]

Stacked timber is quantified in-store units and then adjusted with a conversion factor for volume estimation in cubic meters, which is important for the wood trade in South America. However, measuring large quantities accurately can be challenging. Digital image processing and artificial intelligence advancements offer promising solutions, making research in this area increasingly attractive. This study aims to estimate conversion factors of stacked Eucalyptus grandis timber using supervised image classification with Artificial Neuronal Network (ANN). Measured data and photographs from an experiment involving thirty stacks of timber were used to achieve this. The conversion factor was determined using photographic methods that involved the applications of equidistant points and ANN and subsequently validated with values observed through the manual method. The ANN method produced more accurate conversion factor estimates than the equidistant points method. Approximately 97% of the ANN estimates were within the ±1% error class, even when using low-resolution digital photographs. [ABSTRACT FROM AUTHOR]

There is considerable interest in identifying and locating natural forests as accurately as possible, because they are deemed essential in preventing biodiversity loss. In the boreal region, natural forests contain a substantial amount of dead wood and exhibit considerable variation in tree age, size, and species composition. However, it is difficult to define natural forests in a quantitative manner. This is an issue, for example, in the Finnish national forest inventory. If naturalness could be related to the metrics derived from tree measurements, it would be easier to locate natural forests based on the inventory data. In this study, we investigated the value of metrics computed from tree locations and tree sizes for the characterization of a key aspect of naturalness, namely, structural naturalness as defined in the Finnish national forest inventory. We used L-moments, Gini coefficient, Lorenz asymmetry, and interquartile range to quantify the variations in tree size at the plot level. We summarized the spatial pattern of trees with a spatial aggregation index. We compared the structural metrics, species proportions, and stand age using the classes of structural naturalness described in the Finnish national forest inventory, which have been determined in the field without strict numerical rules. These categories are 'natural', 'near-natural', and 'non-natural'. We found that the forests evaluated as structurally natural had larger variations in tree size and species composition and showed a more clustered spatial pattern of trees on average, although the variation in the structural metrics was considerable in all three classes. In addition, we used the structural metrics to predict naturalness by employing a random forest algorithm. Based on the structural metrics, it was possible to obtain high precision in the classification only if we simultaneously accepted low recall, and vice versa; the link between the inspected metrics and naturalness evaluated in the field was weak. The stand age separated the three classes more clearly and it also improved the classification. [ABSTRACT FROM AUTHOR]

Phoenix acaulis (Roxb.), a common understory shrub in Nepal's Chure region, has remained largely understudied until now. Therefore, this study aims to examine the ecological impact of P. acaulis on Shorea robusta (Gaertn. f.) forest properties and to explore associated community perceptions in S. robusta forest of Udayapur district, Nepal. Stratified random sampling was adopted for this study where P. acaulis density (high, low and absent) was considered as basis of stratification. Altogether, 45 rectangular plots of 10 m x 10 m area (15 each category) were established for regeneration survey and soil sample collection (up to 30 cm). Soil quality index (SQI) method was used for soil quality assessment using indicators on the basis of prior studies conducted in Nepal. Using random sampling, a total of 52 households from the community forest user group were interviewed to gather their insights on the perceived effects of P. acaulis and its management. Highest S. robusta seedling and sapling density was observed in P. acaulis absent area (1132 ± 9.65 ha⁻¹ and 60 ± 0.63 ha⁻¹) where lowest in P. acaulis dense area (548 ± 7.4 ha⁻¹ and 4 ± 0.2 ha⁻¹). Similarly, higher SQI was in areas with high P. acaulis density (0.49) followed by low (0.45) and absent area (0.39). Most respondents advocated for removing P. acaulis from the forest, highlighting significant concerns among stakeholders. Our study suggests a positive impact P. acaulis on soil quality but indicates a negative impact on S. robusta regeneration. Therefore, further research to explore management strategies that balance the positive impact on soil quality with the observed negative influence on regeneration in P. acaulis presence areas is recommended.

Large-scale forest inventory at the individual tree level is critical for natural resource management decision making. Terrestrial Laser Scanning (TLS) has been used for individual tree level inventory at plot scale However, due to the inflexibility of TLS and the complex scene of natural forests, it is still challenging to localize and measure every tree at large scale. In this paper, we present a framework to conduct large-scale natural forest inventory at the individual tree level by taking advantage of deep learning models and Mobile Laser Scanning (MLS) systems. First, a deep learning model, ForestSPG, was developed to perform large-scale semantic segmentation on MLS LiDAR data in natural forests. Then, the forest segmentation results were used for individual stem mapping. Finally, Diameter at Breast Height (DBH) was measured for each individual stem. Two natural forests mapped with backpack and Unmanned Aerial Vehicle (UAV) LiDAR systems were tested. The results showed that the proposed ForestSPG is able to segment large-scale forest LiDAR data into multiple ecologically meaningful classes. The proposed framework was able to localize and measure all 5838 stems at individual tree level in a 20 ha natural forest in less than 20 min using UAV LiDAR. DBH measurement results on trees’ DBH larger than 38.1 cm (15 in) showed that backpack LiDAR was able to achieve 1.82 cm of Root Mean Square Error (RMSE) and UAV LiDAR was able to achieve 3.13 cm of RMSE. The proposed framework can not only segment complex forest components with LiDAR data from different platforms but also demonstrate good performance on stem mapping and DBH measurement. Our research provides and automatic and scalable solution for large-scale natural forest inventory at individual tree level, which can be the basis for large-scale estimation of wood volume and biomass.

Trestima® is a computer vision-based smartphone application that utilises relascope theory to obtain estimates of forest attributes from smartphone photographs. The aim of this study was to investigate the accuracy of Trestima estimation and evaluate whether it is sufficiently accurate for operational use in forestry. Our data consisted of 37 forest stands, encompassing 73.5 ha in southeastern Finland, where Trestima estimates were obtained by forestry professionals during their work. The results were compared with harvester data obtained from clear-cut stands. The number of photographs taken per stand ranged between 1-29 (average: 7.3; standard deviation: 5.0). The total amount of industrial roundwood harvested from the stands was 21.531 m3 and the average harvest removal per hectare was 282 m3. The accuracy of Trestima estimation was relatively good when ≥ 10 photographs per stand were taken. In this case, the root mean square error percent (RMSE%) value associated with roundwood volume was 17.7%. When the number of photographs per stand was < 10, the accuracy of Trestima was much weaker (RMSE% 22.7-55.3%). On average, Trestima underestimated harvested volumes in Scots pine (Pinus sylvestris L.) stands (Bias% 11.4-89.2), although the bias was smaller (Bias% -12.7-12.4) with Norway spruce (Picea abies [L.] Karst.) stands. The Trestima smartphone application is a possible option for traditional field measurements in operational forestry, provided that its usage instructions are strictly followed, which is not always the case in practice.

Windthrow (i.e., trees broken and uprooted by wind) is a major natural disturbance in Amazon forests. Images from medium-resolution optical satellites combined with extensive field data have allowed researchers to assess patterns of windthrow tree-mortality and to monitor forest recovery over decades of succession in different regions. Although satellites with high spatial-resolution have become available in the last decade, they have not yet been employed for the quantification of windthrow tree-mortality. Here, we address how increasing the spatial resolution of satellites affects plot-to-landscape estimates of windthrow tree-mortality. We combined forest inventory data with Landsat 8 (30 m pixel), Sentinel 2 (10 m), and WorldView 2 (2 m) imagery over an old-growth forest in the Central Amazon that was disturbed by a single windthrow event in November 2015. Remote sensing estimates of windthrow tree-mortality were produced from Spectral Mixture Analysis and evaluated with forest inventory data (i.e., ground true) by using Generalized Linear Models. Field measured windthrow tree-mortality (3 transects and 30 subplots) crossing the entire disturbance gradient was 26.9 ± 11.1% (mean ± 95% CI). Although the three satellites produced reliable and statistically similar estimates (from 26.5% to 30.3%, p < 0.001), Landsat 8 had the most accurate results and efficiently captured field-observed variations in windthrow tree-mortality across the entire gradient of disturbance (Sentinel 2 and WorldView 2 produced the second and third best results, respectively). As expected, mean-associated uncertainties decreased systematically with increasing spatial resolution (i.e., from Landsat 8 to Sentinel 2 and WorldView 2). However, the overall quality of model fits showed the opposite pattern. We suggest that this reflects the influence of a relatively minor disturbance, such as defoliation and crown damage, and the fast growth of natural regeneration, which were not measured in the field nor can be captured by coarser resolution imagery. Our results validate the reliability of Landsat imagery for assessing plot-to-landscape patterns of windthrow tree-mortality in dense and heterogeneous tropical forests. Satellites with high spatial resolution can improve estimates of windthrow severity by allowing the quantification of crown damage and mortality of lower canopy and understory trees. However, this requires the validation of remote sensing metrics using field data at compatible scales.

Inventorying forest ecosystems is an essential part of forest management planning. However, it is quite costly and time-consuming, particularly for larger areas. Recently, significant developments have been made in unmanned aerial vehicle (UAV) technology to improve the cost and time efficiency in forest inventory. Therefore, UAV images have become one of the inventory tools that produces data with high spatial resolution in determining forest resources. This study aims to investigate the contribution of UAV data to forest inventory in a case study area with a total of 30 sample plots located in pure and natural Crimean pine (Pinus nigra J.F. Arnold ssp. pallasiana (Lamb.) Holmboe) stands in the Black Sea backward region of Türkiye. Total tree height (h) and stem volume (v) were recorded at individual tree level (n = 367), and the number of trees (N), mean height (hmean), top height (htop), stand basal area (BA) and stand volume (V) were calculated at sample plot level (n = 30) from both the field and UAV-based data. Pearson's correlation coefficients (r) for h and v were 0.96 and 0.72, respectively, the highest correlation at the sample plot level was observed for the hmean - htop (r = 0.96), while the lowest correlation was found for BA (r = 0.54). The suitability of the observation and prediction values was assessed using a t-test at both individual tree and sample plot levels. According to the t-test results, the observation and prediction values for h, v, hmean, htop, BA and V metrics were found to be compatible (p > 0.05), but not for N (p < 0.05). Overall results indicated that UAV technology has a potential to be used in forest inventory and can contribute to the determination of individual tree and stand metrics. Thereby, it saves cost and time in forest inventory studies and helps monitoring the dynamic structure of the forest ecosystem with an effective approach in forest inventory. [ABSTRACT FROM AUTHOR]

Recent advances in sensor and algorithmic technologies have led to the exploration of remote/proximal sensing for automated forest inventory at various scales. For detailed below-canopy mapping to derive critical forest biometrics, such as diameter at breast height (DBH), merchantable height, and debris volume, under-canopy mobile LiDAR mapping is preferred. These mapping systems typically rely on an onboard integrated global navigation satellite system/inertial navigation system (GNSS/INS) unit for point cloud generation. The main challenge of such under-canopy mapping is the intermittent access to the GNSS signal, which is crucial to deriving accurately georeferenced mapping products. Advances in Simultaneous Localization and Mapping (SLAM) offer an alternative to GNSS/INS-assisted georeferencing in GNSS-denied/challenging scenarios. In this study, we propose a general and comprehensive Forest Feature LiDAR SLAM framework that encompasses odometry and mapping threads for a 3D LiDAR unit mounted on backpack systems to achieve accurate forest inventories. In the odometry thread, ground/tree trunk features are extracted from LiDAR scans (i.e., points from a full revolution of the laser beams) and used to estimate the transformation between successive scans. The mapping thread performs local/global least squares adjustment (LSA) using derived features to register LiDAR scans to a common reference frame. One advantage of the proposed framework is that the tree trunk features and ground information – which are critical to forest inventory applications – are directly derived in the SLAM process, making it superior to other geometric feature-based approaches. Additionally, relative trajectory information provided by onboard navigation units and/or reference point clouds from other sources can be incorporated into the process. In this study, three in-house developed backpack systems with varying specifications were used to collect data in complex forest areas. The proposed SLAM strategy was performed on these datasets and compared with point clouds acquired by uncrewed aerial vehicle (UAV) and a commercial backpack LiDAR system. Experimental results suggested that the proposed framework can produce point clouds with satisfactory intra-dataset alignment quality (in the range of 2–4 cm) and positional accuracy (around 10 cm) for all backpack systems. [ABSTRACT FROM AUTHOR]

Precisely estimating the position, diameter at breast height (DBH), and height of trees is essential in forest resource inventory. Augmented reality (AR)-based devices help overcome the issue of inconsistent global point cloud data under thick forest canopies with insufficient Global Navigation Satellite System (GNSS) coverage. Although monocular simultaneous localization and mapping (SLAM) is one of the current mainstream systems, there is still no monocular SLAM solution for forest resource inventories, particularly for the precise measurement of inclined trees. We developed a forest plot survey system based on monocular SLAM that utilizes array cameras and Inertial Measurement Unit (IMU) sensors provided by smartphones, combined with augmented reality technology, to achieve a real-time estimation of the position, DBH, and height of trees within forest plots. Our results from the tested plots showed that the tree position estimation is unbiased, with an RMSE of 0.12 m and 0.11 m in the x-axis and y-axis directions, respectively; the DBH estimation bias is −0.17 cm (−0.65%), with an RMSE of 0.83 cm (3.59%), while the height estimation bias is −0.1 m (−0.95%), with an RMSE of 0.99 m (5.38%). This study will be useful in designing an algorithm to estimate the DBH and position of inclined trees using point clouds constrained by sectional planes at the breast height of the trunk, developing an algorithm to estimate the height of inclined trees utilizing the relationship between rays and plane positions, and providing observers with visual measurement results using augmented reality technology, allowing them to judge the accuracy of the estimates intuitively. Clearly, this system has significant potential applications in forest resource management and ecological research. [ABSTRACT FROM AUTHOR]

The management of plantation forests using precision forestry requires advanced inventory methods. Unmanned aerial vehicle laser scanning (ULS) offers a cost-effective approach to accurately estimate forest structural attributes at both plot and individual tree levels. We examined the utility of ULS data collected from a radiata pine stand for tree detection and prediction of diameter at breast height (DBH) and stem volume, using data thinned to 13-point densities (ranging from 10–12,200 points/m2). These datasets were created using a DTM with the highest pulse density and DTMs that used the native decimated point clouds. Models of DBH were constructed using partial least squares (PLS) and random forest (RF) from seven classes of metrics that characterized the horizontal and vertical structure of the canopy. Individual tree segmentation was consistently accurate across the 13-point densities and was insensitive to DTM type (F1 scores > 0.96). Predictions of DBH using PLS models were consistently more accurate than RF models and accuracy was insensitive to the DTM type. Using data from the native DTMs, DBH estimation using PLS had the lowest RMSE of 1.624 cm (R2 of 0.756) at a point density of 12,200 points/m2. Stem volume predictions made using PLS predictions of DBH and height from the ULS had the lowest RMSE of 0.0418 m3 (R2 of 0.792) at 12,200 points/m2. The RMSE values for DBH and volume remained relatively stable from 12,200 to between 750 and 400 points/m2, with reductions in accuracy occurring as point density declined below this threshold. Overall, these findings have significant implications, particularly for the precise estimation of DBH and stem volume at the individual tree level. They demonstrate the potential of cost-effective ULS sensors for rapid and frequent plantation forest assessment, thereby enhancing the application of light detection and ranging (LiDAR) technology in plantation forest management. [ABSTRACT FROM AUTHOR]

On-the-ground sample-based forest inventory methods have been the standard practice for more than a century, however, remote sensing technologies such as airborne laser scanning (ALS) are providing wall-to-wall inventories based on individual tree measurements. In this study, we assess the accuracy of individual tree height, diameter, and volume derived from field-cruising measurements and three ALS data-derived methods in a 1.1 ha stand using direct measurements acquired on felled trees and log-scale volume measurements. Results show that although height derived from indirect conventional field measurements and ALS were statistically equivalent to felled tree height measurements, ALS measured heights had lower root mean square error (RMSE) and bias. Individual tree diameters modeled using a height-to-diameter-at-breast-height model derived from local forest inventory data and the software ForestView had moderate RMSE (8.3–8.5 cm) and bias (-3.0 – -0.3 cm). The ALS-based methods underdetected trees but accounted for 78%–91% of the field reference harvested merchantable volume and 71%–99% of the merchantable volume scaled at the mill. The results also illustrate challenges of using mill-scaled volume estimates as validation data and highlight the need for more research in this area. Overall, the results provide key insights to forest managers on accuracies associated with conventional field-derived and ALS-derived individual tree inventories. Study Implications : Forest inventory data provide critical information for operational decisions and forest product supply chain planning. Traditionally, forest inventories have used field sampling of stand conditions, which is time-intensive and cost-prohibitive to conduct at large spatial scales. Remote sensing technologies such as airborne laser scanning (ALS) provide wall-to-wall inventories based on individual tree measurements. This study advances our understanding of the accuracy of conventional field-derived and ALS-derived individual tree inventories by evaluating these inventories with felled tree and log scaling data. The results provide key insights to forest managers on errors associated with conventional field and ALS-derived individual tree measurements. [ABSTRACT FROM AUTHOR]

The use of mobile laser scanning to survey forest ecosystems is a promising, scalable technology to describe forest 3D structures at high resolution. To confirm the consistency in the retrieval of forest structural parameters using hand-held laser scanning (HLS), before operationalizing the method, confirming the data is crucial. We analyzed the performance of tree-level mapping based on HLS under different phenology conditions on a mixed forest in western Spain comprising Pinus pinaster and two deciduous species, Alnus glutinosa and Quercus pyrenaica. The area was surveyed twice during the growing season (July 2022) and once in the deciduous season (February 2022) using several scanning paths. Ground reference data (418 trees, 15 snags) was used to calibrate the HLS data and to assess the influence of phenology when converting 3D data into tree-level attributes (DBH, height and volume). The HLS-based workflow was robust at isolating tree positions and recognizing stems despite changes in phenology. Ninety-six percent of all pairs matched below 65 cm. For DBH, phenology barely altered estimates. We observed a strong agreement when comparing HLS-based tree height distributions. The values exceeded 2 m when comparing height measurements, confirming height data should be carefully used as reference in remote sensing-based inventories, especially for deciduous species. Tree volume was more precise for pines (r = 0.95, and relative RMSE = 21.3 –23.8%) compared to deciduous species (r = 0.91 –0.96, and relative RMSE = 27.3–30.5%). HLS data and the forest structural complexity tool performed remarkably, especially in tree positioning considering mixed forests and mixed phenology conditions. [ABSTRACT FROM AUTHOR]

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Given the severe anthropogenic pressure on tropical forests and the high demand for field observations of ecosystem characteristics, it is crucial to collect such data both in pristine tropical forests and in the converted deforested land cover classes. To gain insight into the ecosystem characteristics of pristine tropical forests, regrowth forests, and cashew plantations, we established an ecosystem monitoring site in Phnom Kulen National Park, Cambodia. Here, we present observed datasets of forest inventories, leaf area index, leaf traits of woody species, a fraction of intercepted photosynthetically active radiation, and edaphic and meteorological conditions. We examined how land-use and land-cover change affect species and functional diversity, stand structure, and edaphic conditions among the three land-cover classes. We further investigated relationships between diameters at breast height and tree height, estimated aboveground biomass (AGB), and explored relationships between ecosystem characteristics and AGB. We uncovered some key differences in ecosystem characteristics among the land-cover classes. We also demonstrated the feasibility of locally updating AGB estimates using power law functions. These datasets and findings can contribute to filling data gaps in tropical forest research, addressing global environmental challenges, and supporting sustainable forest management. [ABSTRACT FROM AUTHOR]

Personal laser scanning devices employing Simultaneous Localization and Mapping (SLAM) technology have rightfully gained traction in various applications, including forest mensuration and inventories. This study focuses the inherent stochastic noise in SLAM data. An analysis of noise distribution is performed in GeoSLAM ZEB Horizon for point clouds of trees of two species, Norway spruce and European beech, to mitigate bias in diameter estimates. The method involved evaluating residuals of individual 3D points concerning the real tree surface model based on TLS data. The results show that the noise is not symmetrical regarding the real surface, showing significant negative difference, and moreover, the difference from zero mean significantly differs between species, with an average of −0.40 cm for spruce and −0.44 cm for beech. Furthermore, the residuals show significant dependence on the return distance between the scanner and the target and the incidence angle. An experimental comparison of RANSAC circle fitting outcomes under various configurations showed unbiased diameter estimates with extending the inlier tolerance to 5 cm with 2.5 cm asymmetry. By showing the nonvalidity of the assumption of zero mean in diameter estimation methods, the results contribute to fill a gap in the methodology of data processing with the widely utilized instrument. [ABSTRACT FROM AUTHOR]

This study assessed tree growth variables, above (AGB), below ground biomass (BGB) and total carbon content (TC) sequestered by Parkia biglobosa (Jacq.) Bench. Plantation in Wasangare, Oyo State using non-destructive ground base survey. Tree growth data (Diameter at breast height, Dbh and Tree height, Th) were collected using lacer ace hypsometer and diameter girth tape from 20 temporary sampling plots of size 25 m X 25 m established through systematic transect lines. Diameter size classes (DSC) for the plantation was examined, carbon stock for each DSC was also determine while basal area (m2 ha-1), volume (m3 ha-1), Biomass (Mg ha-1) and Carbon (Mg ha-1) were also estimated. Results showed mean Dbh of 18.7 + 0.25 cm with 8.14 + 0.10 m, 0.033 + 0.00 m2 ha-1 and 0.320 + 0.01 m3 ha-1 for tree height, basal area and volume respectively. AGB and BGB were 10.877 + 0.39 Mgha-1 and 2.175 + 0.08 Mgha-1 respectively while TC was 6.527 + 0.24 Mgha-1. The percentage carbon stock proportion for each DSC revealed class size 25-29-9 cm (19.02%) as the highest while the least proportion was observed in less than 5 cm class with 0.04% of carbon. The DSC showed majority of the tree Dbh in lower Dbh classes with fewer trees in higher classes forming almost a normal bell shape. The study provides information that can help the management in planning silvicultural activities and selective removal from the stand (harvesting schedule).  Tree Dbh, height, basal area, volume and biomass are the determinant characteristics for forest carbon assessment. In conclusion, the plantation actively sequesters carbon showing potentials for indigenous trees in climate change mitigation. [ABSTRACT FROM AUTHOR]

Precise individual tree or sample-based inventories derived from 3D point cloud data of mobile laser scanning can improve our comprehensive understanding of the structure, function, resilience, biodiversity, and ecosystem services of urban forests. This study assessed the performance of a handheld laser scanning system (HLS) for the extraction of tree position, diameter at breast height (DBH), and tree height (H) in an urban area. A total of 2083 trees of 13 species from 34 plots were analyzed. The results showed that the registration of tree positions using ground control points (GCPs) demonstrated high accuracy, with errors consistently below 0.4 m, except for a few instances. The extraction accuracy of DBH for all trees and individual species remained consistently high, with a total root mean square error (RMSE) of 2.06 cm (6.89%) and a bias of 0.62 cm (2.07%). Notably, broad-leaved trees outperformed coniferous trees, with RMSE and bias values of 1.86 cm (6%) and 0.76 cm (2.46%), respectively, compared to 2.54 cm (9.46%) and 0.23 cm (0.84%), respectively. The accuracy of H extraction varied significantly among different species, with R2 values ranging from 0.65 to 0.92. Generally, both DBH and H were underestimated compared to ground measurements. Linear mixed-effects models (LMEs) were applied to evaluate factors affecting the performance of HLS with the plot as a random factor. LME analysis revealed that plant type and terrain significantly influenced the accuracy of DBH and H derived from HLS data, while other fixed factors such as plot area, tree density, and trajectory length showed no significance. With a large sample size, we concluded that the HLS demonstrated sufficient accuracy in extracting individual tree parameters in urban forests. [ABSTRACT FROM AUTHOR]

Conserving plant diversity is integral to sustainable forest management. This study aims at diversifying tools to map spatial distribution of species richness. We develop a sampling strategy of using rapid assessments by local communities to gather prior information on species richness distribution to drive census cell selection by sampling with covariate designs. An artificial neural network model is built to predict the spatial patterns. Accuracy and consistency of rapid assessment factors, sample selection methods, and sampling intensity of census cells were tested in a simulation study with seven 25–50-ha census plots in the tropics and subtropics. Results showed that identifying more plant individuals in a rapid assessment improved accuracy and consistency, while transect was comparable to or slightly better than nearest-neighbor assessment, but knowing more species had little effects. Results of sampling with covariate designs depended on covariates. The covariate I freq, inverse of the frequency of the rapidly assessed species richness strata, was the best choice. List sampling and local pivotal method with I freq increased accuracy by 0.7%–1.6% and consistency by 7.6%–12.0% for 5% to 20% sampling intensity. This study recommends a rapid assessment method of selecting 20 individuals at every 20-m interval along a transect. Knowing at least half of the species in a forest that are abundant is sufficient. Local pivotal method is recommended at 5% sampling intensity or less. This study presents a methodology to directly involve local communities in probability-based forest resource assessment to support decision-making in forest management. [ABSTRACT FROM AUTHOR]

Trunk diameter is related to the overall health and level of carbon sequestration in a tree. Trunk diameter measurement, therefore, is a key task in both forest plot and urban settings. Unlike the traditional approach of manual measurement with a measuring tape or calipers, several recent approaches rely on sophisticated technologies such as LiDAR and time-of-flight cameras that provide fine-grain depth maps, which are used for depth-assisted image segmentation in downstream processing. These technologies are supported only on specialized devices or high-end smartphones. We present a mobile application that uses coarse-grain depth maps derived from an optical sensor, and so can be run on most common Android devices. Moreover, we use a state-of-the-art deep neural network to estimate trunk diameter from an image and its corresponding coarse depth map (RGB-D). We tested our app using a data set collected from four countries and under challenging conditions including occlusion, leaning trees, and irregular shapes and found that our algorithm has a MAE of 1.66 cm and an RMSE of 2.46 cm, which is comparable to accuracy from fine-grain depth maps. Moreover, diameter measurement using our app is >5 times faster than traditional manual surveying.

Introduction: The knowledge about forest growth, influenced by factors such as tree species, tree age, and environmental conditions, is a key for future forest preservation. Height and age data can be combined to describe forest growth and used to infer known environmental effects.Methods: In this study, we built 14 height growth curves for stands composed of monospecific or mixed species using ground measurements and satellite data. We built a random forest height model from tree species, age, area of disturbance, and 125 environmental parameters (climate, altitude, soil composition, geology, stand ownership, and proximity to road and urban areas). Using feature elimination and SHapley Additive exPlanations (SHAP) analysis, we identified six key features explaining the forest growth and investigated how they affect the height.Results: The agreement between satellite and ground data justifies their simultaneous exploitation. Age and tree species are the main predictors of tree height (49% and 10%, respectively). The disturbed patch area, revealing the regeneration method, impacts post-disturbance growth at 19%. The soil pH, altitude, and climatic water budget in summer impact tree height differently depending on the age and tree species.Discussion: Methods integrating satellite and field data show promise for analyzing future forest evolution.

Abstract Forests are a large carbon sink and could serve as natural climate solutions that help moderate future warming. Thus, establishing forest carbon baselines is essential for tracking climate‐mitigation targets. Western US forests are natural climate solution hotspots but are profoundly threatened by drought and altered disturbance regimes. How these factors shape spatial patterns of carbon storage and carbon change over time is poorly resolved. Here, we estimate live and dead forest carbon density in 19 forested western US ecoregions with national inventory data (2005–2019) to determine: (a) current carbon distributions, (b) underpinning drivers, and (c) recent trends. Potential drivers of current carbon included harvest, wildfire, insect and disease, topography, and climate. Using random forests, we evaluated driver importance and relationships with current live and dead carbon within ecoregions. We assessed trends using linear models. Pacific Northwest (PNW) and Southwest (SW) ecoregions were most and least carbon dense, respectively. Climate was an important carbon driver in the SW and Lower Rockies. Fire reduced live and increased dead carbon, and was most important in the Upper Rockies and California. No ecoregion was unaffected by fire. Harvest and private ownership reduced carbon, particularly in the PNW. Since 2005, live carbon declined across much of the western US, likely from drought and fire. Carbon has increased in PNW ecoregions, likely recovering from past harvest, but recent record fire years may alter trajectories. Our results provide insight into western US forest carbon function and future vulnerabilities, which is vital for effective climate change mitigation strategies.

Stand-level forest tree species perception and identification are needed for monitoring-related operations, being crucial for better biodiversity and inventory management in forested areas. This paper contributes to this knowledge domain by researching tree trunk types multispectral perception at stand-level. YOLOv5 and YOLOv8 - Convolutional Neural Networks specialized at object detection and segmentation - were trained to detect and segment two tree trunk genus (pine and eucalyptus) using datasets collected in a forest region in Portugal. The dataset comprises only two categories, which correspond to the two tree genus. The datasets were manually annotated for object detection and segmentation with RGB and RGB-NIR images, and are publicly available. The “Small” variant of YOLOv8 was the best model at detection and segmentation tasks, achieving an F1 measure above 87% and 62%, respectively. The findings of this study suggest that the use of extended spectra, including Visible and Near Infrared, produces superior results. The trained models can be integrated into forest tractors and robots to monitor forest genus across different spectra. This can assist forest managers in controlling their forest stands.

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).

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.

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=0.000085D2.122270H0.666217, and biomass was estimated using the equation AGB=0.0673ρD2H0.976. 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.