Forest Survey Research Articles

Studying Forest Species Classification Methods by Combining PolSAR and Vegetation Spectral Indices

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.

Corydalis pumila a Dél-Tiszántúlon és további adatok a Kis-Sárrét és a Körös menti sík erdei flórájához

This article presents some interesting floristic data from the survey of forests of the ‘Kis-Sarrét’ and the ‘Körös Plain’ microregions (East Hungary) between 2016 and 2024. This article focuses on species associated with mesophilic forests. The majority of the data presented are from the forests of the ‘Kis-Sarrét’ and the ‘Körös Plain’, including some surprising species new to the southern part of the ‘Tiszántúl’ region, including Corydalis pumila in Bélmegyer and Salvia glutinosa in Körösnagy­harsány. Several species typical of mesophilic forests were also found in these two areas, which are extremely rare in the southern part of ‘Tiszántúl’ region, of which only one or two current occurrences are known (e.g. Allium oleraceum, Carex remota, Hesperis sylvestris and Orchis purpurea). In addition to the most interesting data, I report the occurrence of all species associated with mesophilic forests that are not included in the maps of the online database of Hungarian vascu­lar plants, except for a few common species. The level of detail of the 42 species enumerated varies according to their importance. The more interesting species are described in more detail, while for the more common species, only brief occurrence data are given, with some comments where necessary.

Plant Architecture Optimizes the Trait-Based Description and Classification of Vegetation.

Trait-based approaches offer valuable perspectives for vegetation classification, but functional traits struggle to capture resource allocation among competing plants, showing limitations across scales. This study aimed to introduce plant architecture to enhance trait-based vegetation classification. Based on a forest transect survey along China's eastern coast from 2021 to 2023, data from 32 plots of coastal dwarf forests (CDF), characterized primarily by reduced plant height, and normal noncoastal dwarf forests (NCDF) were obtained. Their community characteristics were quantified, and classification and clustering models assessed the advantages of plant architecture in distinguishing these communities. The results indicated plant architecture traits are more critical for distinguishing different community types than leaf-based functional traits. Additionally, plant architecture traits are effective in clustering plant associations within the same community type. Because plant architectural traits are closely linked to habitat, phylogeny and community structure, providing a comprehensive description of vegetation. In contrast, traditional plant functional traits primarily reflect habitat information related to soil nutrients. Our findings underscore the importance of plant architecture in optimizing trait-based vegetation classification and suggest that variations in the plasticity of plant architecture traits may support the classification of CDF as a distinct vegetation unit.

Accuracy Assessment of Advanced Laser Scanner Technologies for Forest Survey Based on Three-Dimensional Point Cloud Data

Forests play a crucial role in carbon sequestration and climate change mitigation, offering ecosystem services, biodiversity conservation, and water resource management. As global efforts to reduce greenhouse gas emissions intensify, the demand for accurate spatial information to monitor forest conditions and assess carbon absorption capacity has grown. LiDAR (Light Detection and Ranging) has emerged as a transformative tool, providing high-resolution 3D spatial data for detailed analysis of forest attributes, including tree height, canopy structure, and biomass distribution. Unlike traditional manpower-intensive forest surveys, which are time-consuming and often limited in accuracy, LiDAR offers a more efficient and reliable solution. This study evaluates the accuracy and applicability of advanced LiDAR technologies—drone-mounted, terrestrial, and mobile scanners—for generating 3D forest spatial data. The results show that the terrestrial LiDAR achieved the highest precision for diameter at breast height (DBH) and tree height measurements, with RMSE values of 0.66 cm and 0.91 m, respectively. Drone-mounted LiDAR demonstrated excellent efficiency for large-scale surveys, while mobile LiDAR offered portability and speed but required further improvement in accuracy (e.g., RMSE: DBH 0.76 cm, tree height 1.83 m). By comparing these technologies, this study identifies their strengths, limitations, and optimal application scenarios, contributing to more accurate forest management practices and carbon absorption assessments.

Integrating Ward’s Clustering Stratification and Spatially Correlated Poisson Disk Sampling to Enhance the Accuracy of Forest Aboveground Carbon Stock Estimation

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.

Yes, they're all individuals: Hierarchical models for repeat survey data improve estimates of tree growth and size

Abstract Repeat measurement surveys of tree size are used in forests to estimate growth behaviour, biomass and population dynamics. Although size is measured with error and individuals vary in their growth trajectories, current size‐based growth modelling approaches do not usually or fully account for both of these features, and therefore under‐utilise available data. We present a new method that leverages the auto‐correlation structure of repeat surveys into a hierarchical Bayesian longitudinal growth model. This new structure allows users to correct for measurement error and capture individual‐level variation in growth trajectories and parameters. To demonstrate the new method we applied it to a sample of tropical tree survey data from long‐term monitoring sites at Barro Colorado Island. We were able to reduce estimated error in size and growth, and extract individual‐ and population‐level growth parameter estimates. We used simulation to evaluate the ability of the new method to improve estimates of growth rate and size, and estimate individual and species‐level parameters. Our method substantially improved the root mean squared error (RMSE) for growth by an average of 61% compared to existing approaches using pairwise differences and reduced RMSE in estimated size RMSE compared to ‘observed’ values in simulated data. Better numerical integration methods (Runge–Kutta fourth order in comparison to Euler and midpoint) provided better estimates of parameters, but did not improve the estimation of size and growth. The choice of a positive growth function eliminated all negative increments without data exclusion. Overall, this study shows how we can gain new and improved insights on growth, using repeat forest surveys. Our new method offers improved biomass dynamics estimation through reduced error in sizes over time, coupled with novel information about within‐species variation in growth behaviour that is inaccessible with species average models, such as individual parameters for the growth function which allows for relationships between parameters to be considered for the first time.

Invasion of Pine Wilt Disease: A threat to forest carbon storage in China

China’s forests, which balance atmospheric carbon (C) levels through photosynthesis, play a crucial role in combating global climate change. The emergence of Pine wilt disease (PWD), caused by the pine wood nematode (PWN, Bursaphelenchus xylophilus), has challenged the stability of these forests, leading to significant tree mortality and disrupting the original ecological balance. However, the impact of PWD on carbon storage and recovery in Chinese forests remains unclear. In this study, we integrated multiple data sources, including forest surveys, remote sensing, and meteorological observations, and applied a method of finely partitioning the resistance of host pine trees across China. Using the MaxEnt model, a live carbon risk model, and a C recovery REGIME model that incorporates disturbance mechanisms, we predicted the forest C risk loss caused by the comprehensive invasion of PWD and assessed the C recovery time for affected forests. We estimate that the total risk of C loss due to PWD invasion under current climate conditions in Chinese forests is 483.23 Tg C, with an average C recovery time of 13.95 years. The main risk areas for PWD are concentrated in the southern coastal regions of China and adjacent provinces, presenting a risk spillover pattern that radiates from focal areas outward. The six provinces with the highest forest risk degree (risk C/total regional C) are, in order, Fujian (13.69%), Zhejiang (9.42%), Hunan (7.49%), Guangxi (7.40%), Jiangxi (7.35%), and Guangdong (7.05%). Our findings indicate that the severe consequences of PWD invasion have transformed affected forests from C sinks to sources. This underscores the urgency of implementing effective measures to block its introduction and spread, thereby promoting the recovery and sustainable development of forest ecosystems.

New records of lichen species found in the National Forest Inventory in South Korea ☆

The National Forest Inventory, conducted at the national level, aims to provide basic data for policy establishment through scientific research and evaluation of forests. The survey was conducted on trees, vegetation, and soil characteristics, in addition to lichen, and research is being conducted as one axis of the forest survey. The survey was carried out in 258 regions, and 16 municipalities from 2021 to 2023. Lichen collected over 3 years included 119 species in 52 genera, 27 families, 16 orders, and four classes, and seven new records were identified. This study aims to provide lichen information known and reports new records of species based on information obtained from ongoing nationwide surveys.

Tracking foresters and mapping tree stem locations with decimeter-level accuracy under forest canopies using UWB

This paper presents a new method for tracking foresters and mapping tree stem locations under forest canopies utilizing ultra-wideband (UWB) data, in which four estimators (collector, estimator, locator, and extractor) operate closely together. The collector records all the distance measurements (beacon to beacon and terminal to the beacons); the estimator determines the local coordinates of the beacons automatically and precisely based on the distance measured between them; the locator then determines the trajectory of the terminal/forester using the extended Kalman filter (EKF) with the assistance of non-line-of-sight (NLoS) distance measurement classification and processing; the extractor continuously determines the optimal locations of the tree stems. To fully evaluate the performance of the developed method, experiments were conducted under four forest canopies with different tree densities and species. The experimental results show that the root-mean-squared-errors (RMSEs) of the proposed method for the terminal/forester positioning and individual tree stem location are less than 0.3 m with a stable update rate of 20 Hz, and a coverage of larger than approximately 3,000 m2 with only four UWB beacons under forest canopies. Therefore, it is sufficient for forest surveys and management with reduced human efforts.

Development and Application of Tree Radial Measurement Device

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.

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

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

Comparison of Three Indoor Viewing Models and On-Site Experiences to Assess Visual Landscape Perception in Urban Forests

Contacting forests in different ways and conducting public perception evaluations of forests are important ways to evaluate forest construction. In order to explore the differences between on-site and manual post-collection indoor evaluations in forest landscape surveys, we combined subjective evaluation and objective indicator monitoring (eye movement characteristics, physiological indicators) based on different forest observation scales. We compared and analyzed the suitability of the following four visual approaches: on-site observation, manual collection, followed by indoor viewing normal photos (NP), videos (VD), and virtual reality panoramas (VR), in terms of public preference, perception, and psychological emotion. The results showed the following: (1) Compared with the on-site evaluation, the three indoor visual approaches (NP, VD, and VR) showed no significant difference in “landscape beauty” and “spatial perception”. VD also showed no significant difference in “landscape perception”, “seeing”, and “plant color preference” and had the strongest substitution for site evaluation. (2) With the exception of small-scale landscapes, in which on-site evaluation showed no substitutability, for the rest of the landscape scales, each of the three indoor visual approaches showed substitutability for on-site evaluation to varying degrees. (3) When conducting physiological and psychological surveys, watching videos and VR are more ideal. In terms of eye tracking, VR is closest to on-site observation. Practice shows that video was closer to on-site observation in most landscape preferences and perceptions. VR was suitable for presenting public visual behavioral characteristics, and NP showed some advantages in landscape beauty and spatial perceptions. The findings of the study can provide a scientific basis for the selection of visual approaches in future landscape evaluation.

Distinct features of topsoil carbon fractions across urban forests in eastern China

AbstractRapid urbanization has increased the areas of urban forests that store considerable soil carbon (C). Different soil C fractions may show distinctive contents and spatial patterns in view of their contrasting sensitivities to various drivers. However, current studies on soil C fractions are mostly limited to natural ecosystems and little is known about the large‐scale patterns and drivers of soil C fractions in urban forests. Based on a field survey of urban forests across a north–south transect in eastern China, we analysed the spatial variations and main drivers of topsoil (surface layer, 0–10 cm; subsurface layer, 10–20 cm) C fractions (i.e., soil organic C, SOC; soil inorganic C, SIC; particulate organic C, POC; mineral‐associated organic C, MAOC). Our results showed that topsoil contents of POC, MAOC and SOC changed non‐linearly with latitude, with lowest values occurring in the cities in the warm temperate region. In contrast, SIC content showed the highest values in the warm temperate region. POC instead of MAOC was found to be a major fraction of SOC in urban forests. The spatial variation in topsoil POC content was mainly explained by mean annual temperature, soil clay and silt content, and park age. The spatial variation in MAOC content was mainly explained by soil clay and silt content, mean annual precipitation, mean annual temperature and park age. In contrast, the spatial variation in SIC content was mainly explained by mean annual precipitation and soil pH. These findings demonstrate distinct features of different soil C fractions in urban forests and provide useful implications for urban soil carbon management.

Mixed stands of black spruce (Picea mariana) and tamarack (Larix laricina) offer high secondary growth in eastern boreal forests of Canada

The Canadian boreal forest is still lacking investigation into compatible species mixtures that can lead to higher growth rates than monospecific or pure stands. The effect of species mixtures on stand productivity can vary with stem density, species proportion, site characteristics, and climate variation. We investigated mixed stands of black spruce (Picea mariana) and tamarack (Larix laricina) in eastern Canadian boreal forests. Using modern forest survey data, we analyzed stand basal area variation in 756 mature stands exclusively composed of these two species over a study area spanning 535,000 km². We generated a linear relationship between mixed stand basal area and stem density, and the residual of this relation was used as response variable named ‘residual basal area’. Positive and negative deviations of residual basal area were analysed with generalized additive models and quantile regressions with tamarack basal area, climate, and site characteristics as explanatory variables. The selected model included tamarack basal area, mean seasonal precipitation, degree days, mean annual temperature, stand age, latitude, longitude, and the type of surficial deposit. Tamarack exhibited a linear positive effect on predicted residual basal area. The most significant effect was observed from the interaction between tamarack basal area and stand age, predicting positive residual basal area of 16 m² ha−1 when stand aged was 150 years old and tamarack basal area was between 12 and 17 m² ha−1. Quantile regressions revealed that tamarack could enhance basal area while increasing black spruce stem size and density. Landscape managers may consider the establishment of mixed black spruce and tamarack stands to favor secondary growth in eastern Canadian boreal forests.

Land Cover Classification of Remote Sensing Imagery with Hybrid Two-Layer Attention Network Architecture

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.

Tree species classification based on PointNet++ deep learning and true- colour point cloud

ABSTRACT Accurate identification of tree species is the foundation of forest resource surveys and an important research field in forestry remote sensing. The introduction of the PointNet++ deep learning network to point cloud processing provides a new approach for tree species identification. This network can directly compute and train unordered point clouds, greatly reducing the manual selection and extraction process of feature data. However, how to establish a high-quality single tree point cloud dataset is a key issue to be solved. In this study, a 2.5-ha temperate coniferous broad-leaved mixed forest located in Mao’er Mountain Experimental Forest Farm in Heilongjiang Province, China, was investigated. The unmanned aerial vehicle (UAV) RGB image and LiDAR synchronous observation system were used to obtain the true-colour point clouds of the forest. Combining upsampling and downsampling methods, a dataset containing coordinates, normal vectors, RGB, and intensity information was constructed from the original point cloud. Comparative experiments were designed based on resampling algorithms, number of sampling points, feature information, and time cost to find the optimal feature information and processing methods for tree species classification. The results showed that the accuracy of tree species classification by using PointNet++ was greatly improved after adding RGB and point cloud intensity information. The classification accuracy was about 5% higher than that using only coordinate data sets. In addition, the combination of PU-Net downsampling network based on point cloud completion and geometric upsampling method achieved the highest classification accuracy (OA = 0.944) when the number of single trees point cloud was 3072. The algorithm also took a relatively shorter running time. This study demonstrated that the introduction of multi-feature information and the optimization of resampling method can provide new solutions for tree species classification based on the PointNet++ deep learning network.

Model-assisted estimation of domain totals, areas, and densities in two-stage sample survey designs

Model-assisted, two-stage forest survey sampling designs provide a means to combine airborne remote sensing data, collected in a sampling mode, with field plot data to increase the precision of national forest inventory estimates, while maintaining important properties of design-based inventories, such as unbiased estimation and quantification of uncertainty. In this study, we present a comprehensive set of model-assisted estimators for domain-level attributes in a two-stage sampling design, including new estimators for densities, and compare the performance of these estimators with standard poststratified estimators. Simulation was used to assess the statistical properties (bias, variability) of these estimators, with both simple random and systematic sampling configurations, and indicated that (1) all estimators were generally unbiased and (2) the use of lidar in a sampling mode increased the precision of the estimators at all assessed field sampling intensities, with particularly marked increases in precision at lower field sampling intensities. Variance estimators are generally unbiased for model-assisted estimators without poststratification, while variance estimators for model-assisted estimators with poststratification were increasingly biased as field sampling intensity decreased. In general, these results indicate that airborne remote sensing data, collected as an intermediate level of sampling, can be used to increase the efficiency of national forest inventories in remote regions.

Food security effects of forest sector participation in rural Liberia

Forests can help rural households cope with food insecurity challenges in the face of climate change while also sequestering carbon and advancing other sustainability objectives in low- and middle-income countries (LMICs). As such, participation in the forestry sector can contribute to achieving the Sustainable Development Goals (SDGs), especially on hunger and food security (SDG 2) in forest rich, but economically poor areas of LMICs. However, analysis of the effects of forest sector participation on food security in such contexts remain limited. Here we estimate the effects of participation in forest-based activities like collection and processing of timber and non-timber forest products (NTFPs) on household food security in Liberia, the most forested country in West Africa. We applied endogenous switching poisson regression to data from 1408 households living in the proximity of forests included in Liberia’s 2019 national household forest survey to estimate the effect of forest sector participation on food security (in terms of reduction in the number of months households reportedly had insufficient food). Results show that forest-sector participation reduced the number of months households had insufficient food by 84% (about 2.7 months). Heterogeneity analyses show that NTFPs affected food security by 80% (about 2.5 months) compared to timber and wood products, which had a 66% effect size (about 2.1 months) on average. Our results provide new, national-scale evidence on the role of forests in enhancing food security in Liberia and suggest the importance of forest sector participation for food security and achieving SDG 2 more generally.

China's future forest carbon sequestration potential under different management scenarios

Enhancing forest carbon storage and carbon sequestration capacity is crucial for achieving carbon neutrality. Scientific forest management can maintain a high level of carbon sequestration capacity in forests, and considering the carbon pool of wood products can extend the time of carbon fixation. However, current predictions of large-scale forest carbon storage and carbon sequestration capacity have overlooked changes in forest carbon absorption with forest age and the buffering effect of wood product carbon pools in the carbon release process. In this paper, we used the Forest Simulation and Optimization System model (FSOS) to analyze the wood supply and carbon sequestration capacities of different management scenarios based on data from the Forest Resources Inventory (2014–2018). The results showed that China's forests could export significant amounts of timber in the future; According to the 9th forest survey report, China's forests produce only 88 million m3 yr-1. Among them, the harvested objects were mainly planted forests, and if all forests (planted forests and natural forests) are involved in forest management planning, the maximum sustainable annual wood supply will reach 286 million m3 yr-1. Moreover, due to the current large proportion of younger forests in China, 358 million m3 of annual wood supply will be achievable in the future (as of the year 2039). Forest management can increase the carbon sequestration capacities in forest ecosystems as well as the wood supply compared to the no management options. In summary, the carbon sequestration potential of unmanaged forests is limited. Appropriate forest management can increase the carbon sequestration potential of forests. The substitution of carbon emission reduction of wood products and bioenergy can also greatly reduce the pressure to achieve carbon neutral strategies.

Extraction of Arbors from Terrestrial Laser Scanning Data Based on Trunk Axis Fitting

Accurate arbor extraction is an important element of forest surveys. However, the presence of shrubs can interfere with the extraction of arbors. Addressing the issues of low accuracy and weak generalizability in existing Terrestrial Laser Scanning (TLS) arbor point clouds extraction methods, this study proposes a trunk axis fitting (TAF) method for arbor extraction. After separating the point cloud data by upper and lower, slicing, clustering, fitting circles, obtaining the main central axis, filtering by distance, etc. The canopy point clouds are merged with the extracted trunk point clouds to precisely separate arbors and shrubs. The advantage of the TAF method proposed in this study is that it is not affected by point cloud density or the degree of trunk curvature. This study focuses on a natural forest plot in Shangri-La City, Yunnan Province, and a plantation plot in Kunming City, using manually extracted data from a standardized dataset of samples to test the accuracy of the TAF method and validate the feasibility of the proposed method. The results showed that the TAF method proposed in this study has high extraction accuracy. It can effectively avoid the problem of trunk point cloud loss caused by tree growth curvature. The experimental accuracy for both plots reached over 99%. This study can provide certain technical support for arbor parameter extraction and scientific guidance for forest resource investigation and forest management decision-making.