72 results about "Forest resource" patented technology

The invention provides a forest resource remote sensing data spatio-temporal change analysis method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the ground object type classification processing of each pixel according to a spectral unmixing processing result, and generating a forest coverage distribution diagram; extracting all pixel center points on the boundary of the change region as an input point set by using the forest coverage distribution map of the plurality of time points; finding out the outermost points in the input point set through iterative calculation, and connecting the points to form a convex polygon; performing standardized integration on the shape and the range of the change area based on a convex polygon to obtain a standardized description result; calculating the annual change rate of the forest area based on the normalized description result; and calculating vegetation coverage and forest biomass ecological indexes based on the annual change rate of the forest area. According to the method, more accurate and coherent monitoring and evaluation of the space-time change of the forest resources can be realized.

The invention discloses a forest resource map modeling method and system, and belongs to the technical field of forestry information. The method comprises the following steps: capturing multi-source heterogeneous original observation data through a sensing unit group deployed in a forest region; performing space-time alignment and quality evaluation on the data by a map generation engine to generate an original observation sequence; calling and analyzing auxiliary geographic information in the environment context library to set prior configuration parameters of the atlas reckoning device; and finally, a map reckoning device is driven to perform fusion reckoning on the observation sequence, and a structured forest resource semantic network is output. The system correspondingly comprises a sensing unit group, an environment context library, an atlas generation engine and an atlas reckoning device. According to the method, full-chain intelligent management of forest resources from precise perception and intelligent cognition to prospective planning is realized through space-based collaborative intelligent perception, a depth generation model of historical knowledge injection and operation simulation based on space-time prediction, and the precision, efficiency and decision support capability of forest resource monitoring are greatly improved.

The invention discloses a forest resource monitoring system based on unmanned aerial vehicle remote sensing, particularly relates to the technical field of forest resource monitoring, and particularly relates to a forest resource monitoring system based on unmanned aerial vehicle remote sensing. The system comprises an unmanned aerial vehicle platform, a remote sensing sensor carried on the unmanned aerial vehicle platform, a data acquisition module, a data processing module and an output module. The core structure of the system further comprises an intelligent data fusion module and a self-adaptive path planning module. The intelligent data fusion module integrates the multi-source remote sensing data through a feature extraction and weight distribution method to generate a unified data set; and the adaptive path planning module dynamically adjusts the flight path according to the real-time environment data to realize coverage optimization and obstacle avoidance functions, so that the data processing efficiency and path planning adaptability of forest resource monitoring are improved.

The invention discloses a method for extracting parameters of a (subtropical) eucalyptus man-made forest based on an unmanned aerial vehicle laser radar. The method comprises the following steps: investigating a sample plot, and measuring and calculating actual forest parameters such as average diameter, average height, sectional area, stand volume and the like; acquiring and preprocessing high-density unmanned aerial vehicle laser radar point cloud data, and extracting three types of feature variables of height, density and vertical structure; deducing a model structural formula on the basis of a stock different-speed growth equation, and screening variables according to rules; constructing a power model by using a regular exhaustion method; the optimal model is screened out through model parameter estimation, inspection, significance analysis and the like. According to the method, the variable synergistic effect is mined through the exhaustion method, the multi-dimensional test system is constructed, and the exclusive model is customized, so that the problems of one-sided variable screening, insufficient model reliability and poor specific forest stand adaptability in the prior art are solved, high-precision estimation of core forest parameters is realized, and the method is suitable for large-scale popularization and application. And scientific and effective technical support is provided for refined management and sustainable operation of eucalyptus man-made forest resources.

The invention relates to a space laser radar forest structure data correction method and system, and belongs to the technical field of space laser remote sensing and forest resource monitoring. Aiming at four kinds of errors of geometric positioning offset, slow distortion, terrain broadening effect and canopy structure influence of GEDI data, UAV control points are arranged by adopting a hierarchical arrangement strategy, a homonymy point relation is established through waveform registration, and weights are calculated; performing overall geometric correction based on the regular grid control points; carrying out local distortion correction based on the encryption control point; establishing a physical compensation model to compensate the terrain broadening effect; based on the canopy structure parameters and GEDI signal quality indexes, establishing a statistical regression model to correct a canopy height residual error; and finally, obtaining a high-precision canopy height convergence result through spatial smoothing processing, and outputting corrected data. The method achieves step-by-step precision improvement from the whole domain to the local domain and from the geometry to the height, and is low in cost, high in precision and wide in application range.

The application discloses a prediction method of tree species structure of arbor forest in subtropical areas, and comprises the following steps: 1) dividing the tree species of arbor forest into three categories of pines, firs and broad-leaved trees, and establishing a prediction model according to volume proportion data of each tree species category obtained from fixed sample plots in previous forest resource surveys; 2) taking two-period survey data as modeling data, and fitting parameters by using STATA software; 3) substituting measured data of each sample plot into the prediction model for iterative calculation, obtaining volume proportion values of the future arbor forest in the two-period interval as prediction values; calculating average prediction values of the prediction values of the tree species proportions of all sample plots; and 4) comparing the prediction value of the volume proportion of the broad-leaved trees or the sum of the prediction values of the volume proportions of the pines and firs in the average prediction values of the tree species proportions with a critical value of coniferous and broad-leaved mixed forest, and determining whether artificial intervention needs to be applied in actual forestry management. The application can predict the tree species structure of arbor forest in future years, and has the function of guiding forestry production and operation activities.

This invention relates to the field of forestry resource monitoring technology, and discloses a dynamic monitoring system and method for forestry resources based on remote sensing imagery. The system includes: generating temporal remote sensing reflectance data within the same grid; generating a continuous canopy occupancy map; generating an infill set; determining the intrinsic neck scale; calculating the topological spectrum intensity of the continuous canopy infill; calculating the interlayer decoupling topological index; generating monitoring judgment results; and outputting change patches. This invention constructs an adaptive multi-scale erosion sequence based on the intrinsic neck scale, combines Eulerian features to quantify the infill topological fragmentation process, and integrates the degree of infill fragmentation with the stability state of the outer envelope through the interlayer decoupling topological index. This achieves accurate determination of change units and precise spatial patch location, avoiding the omissions or misjudgments of deep forest understory structure changes by traditional techniques, ensuring that the monitoring results accurately reflect the actual mechanisms of dynamic changes in forest resources.

The invention relates to an urban forest resource monitoring system and method based on multi-source remote sensing fusion, and the system constructs a sound-vibration cooperative control and edge intelligent processing dual-core system architecture, collects the remote sensing data of an urban complex operation environment in a high-quality manner, achieves the total-factor centimeter-level perception and robust obstacle avoidance of the urban forest complex environment, and achieves the real-time monitoring of the urban forest complex environment. And the operation safety and the data integrity are ensured. On the basis of interpretable AI and multi-modal fusion knowledge acquisition, scale pushing from a single tree to a forest stand is implemented, high-precision intelligent analysis of forest stand scale parameters is completed, and automatic and quantitative inversion of forest stand scale key parameters is realized at an airborne edge end. According to the system, key stand structure factors such as stand density, canopy density, average height and variation coefficient are automatically calculated by aggregating individual tree detection results, single-machine intelligence and group cooperation are realized, and large-scale and high-efficiency urban forest resource checking and dynamic monitoring can be realized.

The invention discloses a tree skeleton branch level division method based on point cloud. The method comprises the following steps: step 1, simplifying a tree skeleton; and step 2, identifying a tree skeleton branch hierarchical structure. According to the method, the point cloud scanned by the ground three-dimensional laser scanner serves as data, the method capable of simplifying the tree skeleton and accurately recognizing the tree skeleton branch hierarchical structure is constructed, the number of branches of the tree skeleton branch hierarchical structure is recognized according to the method, and the recognized number of branches has great significance in forest resource checking.

This invention discloses a method for registering forest point clouds from UAV-LiDAR during leafy and leafless periods, relating to photogrammetry, remote sensing mapping, and forest resource monitoring. The method extracts ground points from two phases of UAV-LiDAR point clouds, constructs multi-directional radial topographic profiles, and extracts local profile segments using a sliding window. It calculates a comprehensive similarity based on trend consistency, robustness to local shape deformation, and curvature similarity, and selects stable matching segments by combining radial distance and topographic undulation amplitude consistency constraints. Three-dimensional corresponding point pairs are generated from the matching segments, and the initial rigid body transformation is solved using SVD. The registration is then optimized using ICP to obtain a multi-temporal fused point cloud. This invention can achieve reliable registration under conditions of phenological differences, missing vegetation structure, and initial biases, providing support for the reconstruction of three-dimensional forest structures and refined resource monitoring.

The invention discloses a flexible bamboo lignin-based polymer material as well as a preparation method and application thereof. Arginine modified polyphenol bamboo lignin and lipoic acid are used as raw materials, and the flexible bamboo lignin-based polymer material with excellent performance is prepared through lipoic acid ring opening polymerization and sulfydryl free radical-polyphenol Michael addition reaction. The preparation method specifically comprises the following steps: carrying out demethylation phenolation modification on bamboo lignin by using a one-pot method, grafting arginine, improving the reaction activity of the bamboo lignin, quantitatively introducing lipoic acid, blocking by using allylurea, and synthesizing the bamboo lignin-based polymer material containing the allylurea, wherein the polymer material contains a dynamic disulfide reversible ring-opening structure. The method breaks through the limitation that the existing lignin is low in reaction activity and lignin-based macromolecules are difficult to recycle, expands the application scene and range of the lignin, and realizes a green mode of oriented preparation, cyclic processing and efficient utilization of the lignin-based macromolecules; and a theoretical basis and guiding significance are provided for high-quality development and high-valued frontier application of the forest resource lignin.

The invention relates to the technical field of forestry resource monitoring, and discloses a forestry resource dynamic monitoring system and method based on remote sensing images, and the method comprises the steps: generating same-grid time phase remote sensing reflectivity data; generating a continuous canopy occupancy map; generating an inner hole set; measuring the scale of the intrinsic neck; the continuous canopy inner hole topology spectrum intensity is calculated; calculating an inter-layer decoupling topology index; generating a monitoring judgment result; and outputting the change pattern spots. According to the method, a self-adaptive multi-scale erosion sequence is constructed on the basis of the intrinsic neck scale, the Euler feature is combined to quantify the inner hole topology fragmentation process, the inner hole fragmentation degree and the outer envelope stable state are integrated through the interlayer decoupling topology index, change unit judgment and space pattern spot accurate positioning are achieved, and the method has the advantages of being simple in structure and convenient to operate. And missing detection or misjudgment of the change of the deep structure under the forest in the traditional technology is avoided, so that the monitoring result conforms to the actual mechanism of the dynamic change of forest resources.

The application provides a forest stand factor estimation method and system based on airborne laser radar data and a terminal, relates to the technical field of image processing, and comprises the following steps: performing normalization processing on laser radar point cloud data of a target forest area to obtain standard point cloud data of the target forest area; extracting a key feature vector in the standard point cloud data; determining a crown volume and a crown surface area of the target forest area according to the key feature vector; and performing forest resource evaluation on the target forest area according to a predicted tree species in the target forest area, the crown volume and the crown surface area to obtain forest stand factors of the target forest area. The application has the advantages of improving the accuracy of estimated forest stand factors.

A large language model driven forest management data processing method belongs to the cross technical field of forest resource management and artificial intelligence data processing. Multi-source data collection and preprocessing, forest management knowledge graph construction, large language model fine tuning and retrieval enhancement generation, data semantic fusion and understanding, management strategy reasoning: based on the fusion data, management intention and industry knowledge, the large language model automatically reasons to form the management strategy conforming to the forest growth law and the multi-objective balance, management scheme text generation: on the basis of completing the strategy and space matching, the forest management scheme text conforming to the forestry industry specification and management requirement is compiled, human-computer interaction and continuous optimization: through the human-computer interaction interface, the feedback information of the forest management scheme of the forestry workers is collected, and the generated management scheme is systematically evaluated from the forest resource sustainability, ecological function improvement, management target achievement degree, risk controllability and policy compliance.

The application discloses a ginkgo tree species identification and dynamic tree height inversion method based on multi-source data fusion, and belongs to the technical field of forest management, forest resource remote sensing monitoring, tree species identification and stand structure parameter. The application comprises the following steps: S1, data preparation and time phase unification; S2, pre-processing and minimum noise separation and noise reduction of high-resolution five satellite image data; S3, ginkgo identification feature construction; S4, ginkgo classification mapping and thematic mask generation; S5, GEDI sample screening and tree height inversion; and S6, precision evaluation and result output. The application can make high-resolution five hyperspectral data, GEDI L2A tree height samples and SAR structure features work collaboratively in a unified framework, so that the ginkgo tree species identification and dynamic tree height inversion are realized, and the growth and health state of trees can be intuitively monitored.

The present application relates to the technical field of remote sensing, in particular to a forest point cloud registration method based on trunk position iteration. The present application makes full use of the natural complementarity of ULS and BLS data in obtaining vertical structure information of forest, converts the traditional discrete point set matching problem into the overlap optimization problem of continuous two-dimensional spatial distribution influence map; by introducing genetic algorithm to construct the spatial mapping relationship between non-homologous data and carry out global optimization, the interference of the horizontal offset of crown and trunk commonly existing in broad-leaved forest on the registration accuracy is fundamentally avoided. The present application can automatically obtain the global optimal solution and generate the homogeneous transformation matrix without laying artificial targets, finally realizes the high-precision rigid alignment of two sets of point clouds, has the advantages of high automation, strong noise-robustness and insensitivity to initial error, and can be widely applied in the fields of high-precision forest resource inventory, digital forestry construction and ecological environment monitoring.

The invention discloses an intelligent forest stand parameter extraction system based on laser radar point cloud data, which belongs to the technical field of laser radar point cloud data processing and forest resource investigation and comprises a multi-scale point cloud preprocessing module, a semantic guidance depth segmentation module, an intelligent parameter calculation module and a self-adaptive quality optimization module. High-precision and high-reliability intelligent extraction of forest stand parameters is realized through deep learning semantic segmentation of a Transform architecture, parameter calculation of confidence coefficient weighting, uncertainty quantification of Monte Carlo sampling and adaptive quality optimization of closed-loop feedback, a deep coupling relationship is formed among modules, the segmentation confidence coefficient directly affects the parameter weight, and the reliability of the forest stand parameters is improved. The uncertainty index triggers closed-loop adjustment, the adjustment parameter is fed back to the front module, a closed-loop optimization path of preprocessing, segmentation, parameter calculation, quality evaluation, adaptive adjustment and reprocessing is formed, and the precision and robustness of forest stand parameter extraction under a complex forest stand structure are remarkably improved.

The invention relates to the technical field of man-made forest sustainable operation and forestry carbon sink management decision optimization, and discloses a man-made forest operation decision method and system based on a carbon sink and economic synergistic effect, which constructs an operation decision model with strong adaptability by combining national forest resource continuous checking data and site index grading. And target weight, market price and intermediate cutting strength are introduced as key decision variables, global optimization is realized by adopting a simulated annealing algorithm, a traditional mode taking wood benefits as a core is broken through, a carbon increment and sink net present value and a wood net present value are brought into a unified optimization system, and cooperation of ecological value and economic benefit is realized. According to the method, collaborative optimization of carbon benefits and economic benefits is effectively realized, and a systematic and generalizable technical path and theoretical support are provided for man-made forest carbon sink operation, carbon asset development and marketization forestry policies.

This invention provides a high-yield method for cultivating *Agaricus bisporus* under forest cover in northern China, relating to the field of edible fungi cultivation technology. The method includes the following specific steps: Step 1: Preparation of the planting substrate. By weight percentage, select 50% whole threshed corn cobs, 15% rice husks, 10% broadleaf tree sawdust, and 25% pre-fermented cow and sheep manure, and prepare the materials for later use. Step 2: Substrate pre-fermentation treatment. In northern regions, pre-fermentation begins in early April. Cow and sheep manure is piled up for pre-fermentation one month in advance. When the pile temperature reaches 60℃, it is turned over, and a total of three turns are performed to complete the pre-fermentation. This invention provides a high-yield method for cultivating *Agaricus bisporus* under forest cover in northern China, making full use of local agricultural and forestry waste and idle forest resources. Through optimized substrate formulation, standardized fermentation control, and a dual-cycle fruiting mode adapted to the northern climate, it achieves high-yield, high-quality, and long-cycle cultivation of *Agaricus bisporus*.

Systems and methods for performing accurate, high-resolution forest inventories within a forest environment. The systems and methods preferably make use of an unmanned aerial vehicle and a ground-based carrier for acquiring first and second initial data frames that are obtained based on point cloud data scanned by a LIDAR mobile mapping system having substantially continuous accessibility to a Global Navigation Satellite System (GNSS) signal.

The invention provides a standing tree cutting degree parameter automatic inversion method based on a ground-based laser radar and deep learning, and relates to the technical field of forest tree measurement. The method comprises the following steps: acquiring an original three-dimensional laser point cloud based on multi-station scanning, and acquiring a clean trunk point cloud; and generating a high-density diameter-height observation data set through axial skeletonization extraction and slicing processing in combination with circle fitting calculation. A variable parameter cutting degree equation model introducing a relative tree height ratio and a height-diameter ratio factor is constructed, and a residual sum of squares objective function is established; and iteratively solving the optimal shape parameter vector by using a nonlinear least square algorithm, and outputting a model parameter of the target stumpage when a precision evaluation index is met. According to the method, the problem of measurement errors caused by branch and leaf shielding in a complex under-forest environment is effectively solved, a traditional destructive survey mode of cutting down and analyzing trees is replaced, non-destructive, high-precision and automatic inversion of standing tree cutting degree parameters is achieved, and the efficiency and the digital level of forest resource monitoring are improved.

The invention relates to the related technical field of forestry science. The forest resource intelligent monitoring method based on multi-source remote sensing and AI comprises the following steps: carrying out space-time alignment and feature level fusion processing on meteorological satellite data, an optical remote sensing image and a laser radar point cloud to generate a multi-dimensional forest environment data cube; based on preset shared social economic path climate scene data, carrying out tree species suitable growth area migration modeling processing, and generating a dynamic migration probability graph; based on a preset tree species physiological parameter library and the dynamic migration probability graph, ecological niche-physiological coupling analysis processing is carried out, and a multi-dimensional vulnerability matrix is generated; and based on the multi-dimensional vulnerability matrix, performing multi-target genetic evolution calculation processing, and generating a climate adaptation type tree species configuration topology scheme, so as to achieve the technical effects of improving the multi-source data fusion capability, optimizing the tree species migration prediction precision and enhancing the ecological adaptability of the configuration scheme.

This invention belongs to the field of forest resource monitoring and growth prediction technology, specifically involving a tree species strategy grouping and forest growth prediction method based on morphological traits. The method includes the following steps: acquiring individual-level monitoring data from sample plots; preprocessing the data to construct growth response variables, and transforming and standardizing the diameter at breast height (DBH) and competition index terms; constructing a species-level morphological trait table, standardizing the morphological traits, and generating morphological representations; clustering tree species based on morphological representations to form tree species strategy groups, and backfilling these strategy groups into individual-level samples to construct a training dataset; establishing a hierarchical growth prediction model based on the training dataset, and setting tree species random effects; when the target tree species is not present in the training dataset, determining the strategy group label based on its morphological representation, and outputting the growth prediction result solely based on the model's fixed effects. This invention is applicable to the monitoring and prediction analysis of multi-species natural forest growth and has good market application prospects.

A forest resource grading and evaluation method integrating artificial intelligence and unmanned aerial vehicles (UAVs) is disclosed. The method employs an evaluation system comprising a system database, a UAV inspection module, a data fusion module, an AI intelligent grading algorithm module, and a planning and adaptation output module. The method includes: system initialization and parameter setting; full-domain data collection via the UAV inspection module; multi-source data preprocessing and standardized fusion via the data fusion module; AI intelligent grading calculation via the AI ​​intelligent grading algorithm module; grading result output and planning adaptation via the planning and adaptation output module; inputting the obtained data into the AI ​​intelligent grading algorithm module for AI model iterative optimization; data storage and system reset. This invention achieves full automation and precision in forest resource grading and evaluation through a closed-loop process of UAV inspection, data fusion, AI intelligent grading, planning adaptation, and model iteration, belonging to the field of forest resource monitoring and evaluation technology.

This invention discloses a method and system for estimating and inverting forest stand volume using remote sensing images, relating to the fields of remote sensing image processing and forest resource surveys. The method includes: acquiring three-dimensional point clouds using UAV lidar and generating a canopy height model through progressive morphological filtering; extracting three-dimensional structural parameters; extracting texture features from near-infrared and red-edge bands of multispectral satellite imagery based on the gray-level co-occurrence matrix; fusing the three-dimensional structural parameters and texture features and adaptively filtering them through a closed-loop feature importance feedback mechanism; and constructing a stochastic forest regression model with measured ground volume as the dependent variable to perform pixel-by-pixel volume inversion and generate a spatial distribution map. This invention overcomes spectral saturation limitations and improves the estimation accuracy of high-volume forest stands.

This invention proposes an interactive tree height development trend prediction method and system at the single-tree scale, relating to the fields of remote sensing information processing and forest resource monitoring. Addressing the problems of existing technologies, such as time-consuming and labor-intensive forest surveys, difficulties in large-scale implementation in areas with significant topographic relief, limited transportation, or scattered tree distribution, and challenges in meeting the needs for refined and continuous monitoring, this invention provides a technical solution at the single-tree scale. It integrates tree species information, age inversion results, and growth models, allowing users to predict the tree height development trend of target trees through spatial interaction. This enables refined analysis and dynamic management of the tree growth process. It effectively avoids the averaging problem associated with traditional plot-scale models and significantly improves the scientific rigor and accuracy of single-tree resource management and forestry management decisions.

A method, computer equipment, medium, and product for calculating forest stand density are disclosed, relating to the field of forest resource monitoring. The method includes: acquiring three-dimensional point cloud data of the forest area to be measured; separating the three-dimensional point cloud data into a ground point set and a vegetation point set; performing elevation normalization processing on the vegetation point set based on the ground point set to obtain a vegetation point cloud; constructing a canopy height model based on the vegetation point cloud, and extracting the local highest points from the canopy height model and adding them to a candidate vertex set for individual trees; constructing a planar Delaunay triangulation network based on the candidate vertex set for individual trees, and determining the vertex pairs connected by each edge in the planar Delaunay triangulation network as adjacent vertex pairs; calculating the branch topological adhesion of adjacent vertex pairs; if the branch topological adhesion is greater than a preset adhesion threshold, identifying false vertices in the adjacent vertex pairs and removing them from the candidate vertex set for individual trees to obtain a true vertex set for individual trees; counting the total number of vertices in the true vertex set for individual trees, and dividing the total number of vertices by the total area of ​​the forest area to be measured to obtain the forest stand density calculation result.

The application provides a forest resource remote sensing data spatio-temporal change analysis method and system, relates to the technical field of data processing, and comprises the following steps: according to the result of spectral unmixing processing, performing ground object type classification processing on each pixel to generate a forest coverage distribution map; using the forest coverage distribution maps at multiple time points, extracting all pixel center points on the boundary of a change region as an input point set; finding out the points located at the outermost periphery in the input point set through iterative calculation and connecting the points to form a convex polygon; normalizing and integrating the shape and range of the change region based on the convex polygon to obtain a normalized description result; calculating the annual change rate of the forest area based on the normalized description result; and calculating the vegetation coverage and forest biomass ecological indexes based on the annual change rate of the forest area. The application can realize more accurate and continuous monitoring and evaluation of the spatio-temporal change of forest resources.

The invention discloses a method for automatically and quantitatively estimating a forest vertical structure by an airborne laser radar, and the method comprises the steps: determining a plurality of voxels based on the unmanned plane laser radar point cloud data of a forest object; based on each voxel, determining a penetration index profile feature map; determining the plant area index of each height layer of the forest object on the basis of taking the penetration index profile feature map as the input of a Beer law inversion model; and determining a vertical structure of the forest object based on the plant area index of each height layer of the forest object. According to the method, the laser echo penetration principle is utilized, the plant area index of each height layer of the forest object is directly inverted through the Beer law inversion model, a layering rule does not need to be manually set, the result is more objective and consistent, the applicability is higher, the efficiency is higher, and the method can be widely applied to actual tasks such as structure investigation and ecological monitoring of multiple types of forest resources.

This invention discloses a method for extracting the crown volume of a single tree from ground-based laser point clouds based on spherical coordinate integration, belonging to the field of forestry engineering technology. First, a point cloud segmentation method using a visual optimal threshold selection strategy is employed to extract the crown point cloud, improving the extraction and operability. Then, the crown points are projected from the 3D point cloud space to the 3D spherical coordinate space, innovatively proposing a new method for finely describing crown morphology. Finally, the crown is cut into several quadrangular pyramidal micro-elements and integrated to achieve crown volume extraction, improving the accuracy and universality of the extraction. This method provides a complete technical workflow for extracting the crown volume of a single tree from ground-based laser point clouds, contributing to improving the automation and intelligence level of forestry production and management. It has significant theoretical and engineering application reference value for forestry scientific research and forestry production, such as 3D tree morphology simulation, forest resource surveys, and aboveground biomass estimation.