45 results about "Diameter at breast height" patented technology

The invention relates to a forest tree diameter at breast height and tree height measurement method based on double-layer unmanned aerial vehicle laser radar point cloud fusion in a complex under-forest environment, and belongs to the technical field of forestry measurement. According to the method, multi-source point cloud registration fusion is carried out based on feature point matching and a weighted fusion algorithm; generating a ground DEM and carrying out point cloud height normalization; carrying out individual tree segmentation by adopting an improved mark control watershed algorithm; constructing a multi-dimensional feature vector for each tree, and calculating each feature; extracting a tree height based on the fused point cloud; an effective point cloud coverage index is introduced to judge the effectiveness of a 1.3 m height point cloud, weighted least square circle fitting is adopted to directly extract the diameter at breast height, and a pre-trained multi-feature fusion gradient lifting model is adopted to estimate the diameter at breast height for a tree with point cloud missing. According to the method, complete stand three-dimensional data is obtained through multi-source point cloud fusion, a multi-dimensional feature system is constructed, a complete mathematical calculation framework is established, and the technical problem that the diameter at breast height cannot be accurately measured due to complex under-forest shrub and grass shielding is effectively solved.

The invention relates to the technical field of forestry management, in particular to a forest and grass resource dynamic monitoring system which comprises the steps that a graph attention network is adopted to conduct high-low weight recognition processing on the structure difference value between node pairs, feature vectors are established through the canopy height difference and canopy density difference between adjacent nodes, and the canopy height difference and the canopy density difference between adjacent nodes are obtained; an edge weight scoring system is constructed in the form of segmented statistics and proportion weighting, node edge pairs with high influence relation strength are dynamically screened, ordered aggregation of spatial communication strength is realized, a graph neural network is introduced in a parameter fusion stage to construct a node parameter representation mechanism, and the spatial communication strength is improved. The tree species proportion, the grade of diameter at breast height and the community vertical structure in a regional sample plot are used as input features, unified mapping of node features of each region is completed in multiple rounds of iteration, cross-regional difference analysis is executed on model output in combination with biomass change frequency, parameter items with the difference proportion lower than a set threshold value are replaced with unified expression, and the model output is obtained. And parameter synchronization is realized to realize space nesting and feature integration.

The invention relates to a high-precision single tree segmentation method based on multi-source point cloud data, and the method comprises the steps: building a high-density point cloud data set through the cooperative collection of an unmanned plane and a knapsack laser radar, carrying out the data preprocessing through the multi-site ICP registration and CSF filtering, carrying out the semantic segmentation based on an Enhanced Tree Instance Net network, and carrying out the segmentation of a single tree. Through feature contribution analysis and dynamic scene adaptive optimization segmentation data, semantic and geometric features are fused to realize individual tree instance segmentation, a physical constraint neural network is introduced to realize multi-parameter collaborative inversion of tree height, diameter at breast height, crown breadth, tree age and the like, it is ensured that the parameters conform to tree growth rules, precision evaluation and calibration are performed through a multi-level index system, and tree growth accuracy is improved. And finally realizing visual expression of a segmentation result and forest stand parameters. According to the method, the individual tree segmentation precision and the growth rule rationality are remarkably improved, and the method is suitable for urban forest carbon sink accurate measurement and intelligent forestry management.

The invention relates to the technical field of forest resource investigation and monitoring, and discloses a natural forest multi-source point cloud registration method and system based on point cloud segmentation optimization, and the method comprises the steps: laying a sample plot, collecting the diameter at breast height, the height and the crown breadth of a single tree, and obtaining the space coordinates of the single tree in combination with RTK; according to the method, unmanned aerial vehicle laser scanning and backpack laser scanning are adopted to collect and preprocess point cloud data of a research area, an ICP algorithm is adopted to align a source point cloud with a target point cloud through rotation and translation, point cloud registration is achieved through a minimization error function, point cloud registration precision and errors are quantified through evaluation indexes, and point cloud registration evaluation is carried out. Therefore, the point cloud registration precision is effectively improved through a segmented registration strategy, limitation caused by single sensor data can be overcome through fusion of BLS and ULS multi-source point cloud data, the extraction precision of tree structure parameters is remarkably improved, and the method is not only suitable for point cloud registration of natural forests, but also suitable for point cloud registration of natural forests. And reliable data support and technical support are provided for forest resource monitoring and ecological protection.

The invention discloses a forest tree DBH (diameter at breast height) inversion method based on a multispectral remote sensing image, which relates to the technical field of remote sensing information inversion, and comprises the following steps of: performing radiometric calibration and geometric correction processing on original multispectral observation data, generating a standardized multispectral data set, and performing single tree identification and canopy segmentation processing to obtain a tree DBH (diameter at breast height); generating single-plant tree crown image data; combining the illumination direction features with the red-edge wave band and the near-infrared wave band, performing multi-scale feature analysis and fusion processing, and outputting canopy feature data; performing nonlinear processing and illumination correction on the canopy feature data to generate breast diameter prediction data; and carrying out space mapping and visual mapping processing on the diameter at breast height prediction data to generate a diameter at breast height distribution diagram result, carrying out precision evaluation and quality inspection processing, and outputting a forest tree diameter at breast height inversion result. The stability, the precision and the environmental adaptability of the breast diameter inversion are improved, and high-reliability technical support is provided for forest structure monitoring and resource evaluation.

The invention discloses a method and a system for automatically measuring the diameter at breast height of a forest based on a millimeter-wave radar network, particularly relates to the technical field of radar signal processing and target detection, and is used for solving the problem of inaccurate forest identification and diameter at breast height measurement caused by false point clouds generated by multiple signal reflections of the existing millimeter-wave radar in a complex forest environment. An initial three-dimensional point cloud is obtained by deploying a millimeter wave radar network, and potential trunk points and suspected false points are identified; suspected points which are gathered around tree trunk anomalies are screened out, and multi-view observation angle information of the suspected points is obtained through network multi-node observation geometry; fitting the optimal spatial position of each point and calculating an observation residual error; and finally, screening and eliminating geometrically inconsistent false points according to residual distribution, and realizing accurate identification and breast diameter extraction of the forest based on the purified point cloud. Interference of multipath reflection false point clouds is effectively suppressed, and reliability and precision of automatic breast diameter measurement of a millimeter wave radar network in a dense forest region are improved.

The invention relates to the technical field of ecological restoration, in particular to an ecological restoration method for a marsh wetland patrol road slope. Experiments show that compared with other tree species (salix procumbens), the survival rate of the salix procumbens in the marsh wetland patrol road side slope is higher, and the salix procumbens has a more excellent slope fixing effect. Furthermore, the length and the thickness of the willow branches are optimized and adjusted, the willow branches with the proper diameter at breast height and the proper length are finally selected for cuttage, the survival rate is remarkably increased, and the slope fixing effect is remarkably improved. Water and soil loss can be effectively reduced, stable vegetation coverage can be formed in a short time, and reliable technical support is provided for ecological restoration of marsh wetland patrol road slopes.

The utility model relates to the technical field of forestry investigation, and discloses a tree breast diameter measuring device for forestry investigation, which comprises a long rod, a box body is arranged at the upper end of the long rod, a cog belt is arranged in the box body, two belt wheels are symmetrically arranged in the cog belt, the two belt wheels are both rotatably arranged in the box body, and the cog belt is arranged in the box body. Two buckles are arranged on the toothed belt, the two buckles and the two belt wheels are arranged at intervals, one side of each buckle is fixedly connected with a sliding block, the sliding blocks are arranged on a sliding way in the box body in a sliding mode, the two sliding blocks are each provided with a clamping rod, one belt wheel is connected with a motor, and the other belt wheel is connected with a clamping rod. On the basis that the diameter at breast height of a trunk at a lower position can be conveniently measured, the front end of the long rod is lifted to the trunk at a higher position, the two clamping rods are close to each other until the trunk is clamped, the diameter at breast height of the tree is obtained through the distance between the two clamping rods, the diameter at breast height of the trunk at the higher position can be conveniently measured, and the application range of the measuring device is expanded.

The invention discloses a tree growth amount three-dimensional form intelligent generation system and method based on multi-dimensional data, and relates to the technical field of plant three-dimensional modeling and forestry information. Tree height, diameter at breast height, ground diameter, multi-year climate and east-south-west-north four-direction annual ring spacing data are collected through the data input module, and after the data are processed through the data processing module, radial annual ring width data and historical environment data are combined through the growth inversion and fusion module. A spatial historical growth difference coefficient matrix is calculated through inversion of a spatial growth inversion model, after a three-dimensional morphology generation module receives the matrix, a final growth difference coefficient is generated in combination with a species inherent morphology parameter matrix, and a parametric modeling engine is driven to generate three-dimensional models of branches, leaf clusters and root systems in a differentiated mode. The result output module outputs a parameterized three-dimensional model; the problems that traditional tree three-dimensional modeling neglects orientation growth difference and is weak in environment relevance are solved, and differentiation tree three-dimensional form intelligent generation is achieved.

The utility model belongs to a tree diameter at breast height and height surveying device, and particularly relates to the technical field of tree surveying, which comprises a machine body, a height measuring assembly is arranged on the outer top surface and the left end of the machine body, a diameter at breast height measuring assembly is arranged on the left end of the outer bottom surface of the machine body, and a grip is fixedly arranged on the right end of the outer bottom surface of the machine body. A square plate is fixedly connected to the outer bottom face of the grip, a clamping and fixing assembly is arranged on the outer wall of the left side of the square plate, a rope can be wound around and attached to a tree by a circle through the arranged positioning square cylinder and then is connected with a rope head in a clamped mode, then the rope is prevented from being automatically recycled, and therefore the situation that the rope head needs to be continuously pulled is avoided; and the workload of a user is reduced.

The invention discloses a larch single tree segmentation and phenotype extraction method based on unmanned aerial vehicle multi-source remote sensing, and relates to the technical field of forestry remote sensing. The method comprises the following steps: firstly, synchronously acquiring visible light, a multispectral image and laser radar point cloud data through an unmanned aerial vehicle; then, a dynamic sparse gating multi-modal feature fusion network is constructed, key features of different data sources can be adaptively focused for effective fusion, and accurate larch individual tree instance segmentation is achieved; and finally, automatically extracting phenotype parameters such as tree height, crown breadth, canopy area and diameter at breast height based on a segmentation result. According to the method, multi-source data fusion, individual tree segmentation and phenotype extraction are integrally completed through an end-to-end deep learning model, the problem of accurate segmentation of larch individual trees in a complex scene is solved, and the efficiency and precision of forestry investigation are remarkably improved.

This invention belongs to the field of geographic information technology, specifically relating to a non-destructive estimation method for standing timber volume based on LiDAR. The method includes: acquiring three-dimensional information of a portion of sample trees to obtain point cloud data; manually measuring a portion of the sample trees after pruning to obtain manual measurement parameters; preprocessing the point cloud data; calculating the volume of the base segment of the sample trees based on the preprocessed point cloud data; calculating shape parameters based on the manual measurement parameters; calculating the volume of the upper segment of the sample trees using the BCTEM model based on the shape parameters and point cloud data; calculating the sum of the base segment volume and the upper segment volume as the standing timber volume of the sample trees; calculating the tree height and diameter at breast height (DBH) of the sample trees; constructing a standing timber volume equation based on the standing timber volume, DBH, and tree height of the sample trees; and using the standing timber volume equation to achieve non-destructive estimation of the standing timber volume of trees similar to the sample trees. This invention solves the problem of inaccurate or impossible trunk measurement due to canopy shading and has good application prospects.

This invention relates to the field of forestry technology and discloses a method for regulating the cultivation of *Machilus chinensis* in forest gap microenvironments. The method includes: constructing a digital twin of *Machilus chinensis* plants by fusing lidar point cloud data, multispectral time-series data, and soil microenvironment parameters; calculating a comprehensive competition index based on three dimensions: light shading, nutrient competition, and spatial expansion potential; generating a graded thinning priority sequence by combining forest gap boundary characteristics and target retention density; and outputting a structured instruction set containing geographic coordinates, diameter at breast height (DBH) grade, and felling order. This invention achieves quantitative, precise, and traceable thinning operations, improving the growth efficiency and resource utilization level of *Machilus chinensis*.

This utility model discloses a novel tree diameter at breast height (DBH) measuring device, comprising a first arc-shaped clamping rod and a second arc-shaped clamping rod. One end of the first arc-shaped clamping rod is a first measuring head, and the other end is a first grip handle. One end of the second arc-shaped clamping rod is a second measuring head, and the other end is a second grip handle. The first and second arc-shaped clamping rods are hinged together by a hinge structure. An elastic contact head is provided on one side of both the first and second measuring heads. A locking component is provided on the hinge structure. This utility model offers high measurement efficiency, accurate measurement results, and simple operation.

The application relates to a forest tree annual growth data remote measurement method based on image recognition, which comprises the following steps: a multispectral image recognition device is controlled by a positioning algorithm to be aligned to a measured tree, and the current coordinates of the measured tree are recorded; the images of multiple spectrums of the measured tree are acquired; the multiple spectrums are recorded at the same time and have time point information; pixel cutting is performed on the multispectral images, the image edge feature mismatch area is removed, and a multispectral image set with completely coincided features is obtained; image normalization calculation is performed, and a normalized image is regenerated according to the normalization calculation result; the pixel numbers of tree height H, crown width A and diameter at breast height C are acquired, and the actual values of the tree height H, the crown width A and the diameter at breast height C are calculated; the RGB feature information of tree leaves is acquired, the specific growth cycle T of the measured tree is determined, and the annual growth data of the measured tree is acquired to obtain a growth model Phi of the measured tree; and data generation comprises the tree height H, the crown width A, the diameter at breast height C and the growth cycle T.

The invention relates to a method for calculating diameter at breast height by establishing a curve model of a trunk shape below a tree chest height, which comprises the following steps of: S1, selecting an investigation area in which trees of the same type are distributed in a concentrated manner, randomly selecting a plurality of trees of the same type in the investigation area as sample trees, and measuring the sample trees to obtain an original measurement data set; s2, selecting a plurality of models and a plurality of fitting software; s3, preprocessing the original measurement data set; s4, substituting the preprocessed data into a plurality of models, performing trunk shape curve fitting, finally obtaining a plurality of fitting models, and calculating fitting errors according to test indexes; s5, analyzing and checking each fitting model according to the fitting error, and screening out an optimal model; and S6, performing trunk analysis on the sample wood according to the optimal model. According to the method, the volume of the felling wood can be accurately calculated by accurately measuring the diameter at breast height of multiple parts below the breast height of the sample wood and screening the optimal model in combination with multi-model fitting and scientific examination by taking the Chinese fir in the Fushan City as an example.

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.

The invention provides a tree biomass calculation method and system. The method comprises the steps of obtaining a database file selected by a user and tree parameters input by the user; wherein the database file is created and stored in advance according to a data file selected by a user; the tree parameters at least comprise diameter at breast height, tree height and tree species; extracting a biomass model and calculation parameters corresponding to the tree species from the database file; inputting the diameter at breast height, the tree height and the calculation parameters into a biomass model for calculation, and judging whether an abnormal condition exists in the calculation process or not; if an abnormal condition exists in the calculation process, processing the abnormal condition to obtain a calculation result; and if no abnormal condition exists in the calculation process, obtaining a calculation result. Therefore, the efficiency of biomass measurement and calculation is effectively improved through database management and an automatic calculation technology.

This application relates to a method and system for detecting the diameter at breast height (DBH) of individual trees in forests based on the organic fusion of multi-source sensors, belonging to the field of forestry remote sensing technology. It performs terrain filtering, orthorectification, and independent tree instance segmentation on airborne lidar point cloud data and multispectral satellite imagery, respectively. Then, through local semantic topology graph construction and cross-modal graph matching, object-level identity binding is achieved to obtain a set of homologous binding pairs for individual trees. Subsequently, a Gaussian weight field for the tree canopy is constructed using the 3D point cloud spatial density distribution of the bound trees. A pixel-by-pixel probabilistic soft-assignment weighting is performed on the multispectral image object blocks to generate a soft-fusion feature vector for individual trees. Finally, this feature vector is input into a regression model constrained by the forest allometric growth law for DBH detection. This application effectively suppresses edge noise pollution caused by spatiotemporal misalignment of multi-source data through object-level flexible anchoring and probabilistic feature fusion, improving the accuracy and robustness of DBH detection in complex forest areas.

The invention provides a forest individual tree parameter automatic extraction method based on point cloud and deep learning, and the method is used for the extraction of the diameter at breast height and position data of a forest individual tree. The method comprises the following steps: S1, data acquisition and preprocessing; s2, multi-height-layer point cloud slicing is carried out; s3, performing point cloud slice imaging and anti-truncation enhancement processing; s4, marking the diameter at breast height and training the model; s5, preliminarily detecting the diameter at breast height, resolving the position and cutting off and removing; s6, multi-height-layer data fusion, false detection elimination and modeling sample identification are carried out; and S7, constructing a DBH (diameter at breast height) prediction model. S8, detecting and complementing the diameter at breast height data; s9, single tree segmentation and tree height extraction are carried out; according to the invention, image boundary truncation interference can be effectively overcome, and efficient, automatic and high-precision extraction of stumpage diameter at breast height and position data is realized.

The utility model belongs to the field of plant breast diameter measurement, and particularly relates to a plant breast diameter measurement statistical device which comprises a positioning shell, a storage mechanism is arranged on one side of the positioning shell, a fixed guiding rule is fixedly connected to the surface of the positioning shell, and a toothed plate is fixedly installed in an inner cavity of the positioning shell. A positioning frame is arranged in an inner cavity of the positioning shell, and the surface of the positioning frame makes contact with the inner wall of the positioning shell. By arranging the storage mechanism, a worker can conveniently carry recording paper for counting the diameter at breast height of a plant, so that the portability of measurement and counting is improved, by arranging the positioning mechanism and shifting two conical clamping plates to move reversely, a positioning shell can be fixed to the surface of the plant, and thus the stability of measuring the diameter at breast height of the plant is improved; the problem that errors exist in measurement results due to the fact that a traditional device for measuring the diameter at breast height is usually a manual hand-held measuring tool for measuring the diameter at breast height of a plant and the manual hand-held mode is poor in stability is solved.

The invention discloses a tree diameter at breast height auxiliary measurement system and method, and relates to the technical field of diameter at breast height measurement. A navigation auxiliary module is used for loading a navigation file of a corresponding measurement area before the operation of measurement personnel, and all trees in a to-be-measured state at present are selected as target trees in sequence for auxiliary navigation; the problem of target loss caused by dense trees in a measurement area by measurement personnel is thoroughly avoided, whether detour exists or not is judged by obtaining the real-time latitude and longitude of any tree reaching the target tree when the tree is determined as the target tree, and a virtual auxiliary tree is selected to be added in front of the tree according to the judgment result. When the next measurement task is executed again, a visual guide arrow is generated to navigate to the auxiliary tree firstly and then navigate to the auxiliary tree, so that the problem of navigation interference caused by misleading of the visual arrow due to the fact that the detouring requirement of the forest area cannot be recognized in traditional navigation is effectively solved, and the safety of auxiliary navigation is further improved.

The invention discloses a hydraulic safety estimation method and device based on tree morphological structure parameters, a medium and a product, and relates to the technical field of ecology and forest management, and the method comprises the steps: obtaining to-be-measured tree data; the to-be-measured tree data comprises tree height, branch height and diameter at breast height; calculating a morphological structure index of the to-be-measured tree data according to a formula; wherein I represents a morphological structure index, H represents a tree height, CBH represents an under-branch height, and DBH represents a diameter at breast height; and substituting the morphological structure index into an empirical model to obtain a hydraulic safety index of the to-be-tested tree data. The quantitative relation between the tree morphological structure and the hydraulic vulnerability is disclosed for the first time, compared with the prior art, the method does not need experimental instruments, the hydraulic characteristics can be estimated only through conventional forestry measurement data, and a scientific basis is provided for artificial forest pruning, density regulation and control and drought resistance management.

The application discloses a forest tree diameter at breast height inversion method based on multispectral remote sensing images, relates to the technical field of remote sensing information inversion, and comprises the following steps: performing radiation calibration and geometric correction processing on original multispectral observation data, generating a standardized multispectral data set, and performing single-tree recognition and crown layer segmentation processing to generate single-tree crown image data; combining the illumination direction feature with the red edge band and the near-infrared band through multiscale feature analysis and fusion processing to output crown layer feature data; performing nonlinear processing and illumination correction on the crown layer feature data to generate diameter at breast height prediction data; performing spatial mapping and visual mapping processing on the diameter at breast height prediction data to generate diameter at breast height distribution map results, and performing precision evaluation and quality inspection processing to output forest tree diameter at breast height inversion results. The stability, precision and environmental adaptability of diameter at breast height inversion are improved, and high-reliability technical support is provided for forest structure monitoring and resource evaluation.

This application provides a method, apparatus, equipment, and medium for measuring individual tree stand parameters, belonging to the field of forest resource survey and forestry remote sensing technology. The method includes: acquiring visible light images, laser point cloud data, and individual tree IDs for each tree within a target area; converting the visible light images and laser point cloud data to a global geographic coordinate system; performing canopy segmentation, tree species classification, and occlusion estimation on each visible light image; performing individual tree point cloud segmentation on each laser point cloud data; matching the canopy segmentation mask with the point cloud clusters based on the individual tree ID to obtain successfully matched 3D contour point sets and 3D projected contour point sets; thereby extracting the target parameters for each tree; obtaining the diameter at breast height (DBH) parameter based on the target parameters; and determining the individual tree volume using a univariate volume model, thereby determining the stand volume. This application improves the accuracy of individual tree parameter extraction and the precision of stand volume measurement.

The application discloses a method for determining the age of single trees in natural forest stands, comprising the following steps: selecting reserved trees with multi-period measured diameter at breast height (DBH) in the natural forest stand, classifying the trees according to the annual growth rate of DBH, constructing a dummy variable of the tree grade based on a theoretical growth equation and adopting a starting point constraint, establishing a statistical model of the multi-period DBH of the single tree and the corresponding investigation time interval, and inversely deducing the age of the single tree through the model. The application solves the problem of obtaining the age of the single tree in the natural forest stand without affecting the growth of the tree and reducing the investigation work by combining the multi-period DBH data of the reserved tree with the differentiation condition of the trees in the forest stand, provides the age data for establishing the growth model of the natural forest reserves, and is helpful for monitoring the dynamic change of the natural forest resources and evaluating the site quality of the forest stand.

In some embodiments, a method involves receiving remote data of a geographic area, generating a canopy height model from digital surface and terrain data, identifying individual trees using a segmentation model that integrates optical and LiDAR data, determining tree heights from the canopy height model, converting heights to diameter at breast height (DBH) values, classifying tree species by segregating them by family and identifying species, calculating biomass using DBH, height, and species-specific factors, and estimating carbon dioxide sequestration based on the calculated biomass.

The utility model discloses a tree DBH (diameter at breast height) measuring device for forestry investigation, and relates to the field of forestry investigation, the tree DBH measuring device comprises a clamping part used for clamping a tree and further comprises a supporting part, the clamping part is arranged at the upper end of the supporting part, and an adjusting assembly used for adjusting the clamping range of the clamping part is arranged at the lower end of the supporting part. And the adjusting assembly is arranged at the lower end of the supporting part, so that an operator can conveniently adjust the clamping range of the clamping part at the lower end of the supporting part, the arm of the operator does not need to be kept at a high position for a long time, and operation is facilitated.

The invention discloses a man-made forest single wood volume estimation method based on an unmanned aerial vehicle laser radar, and belongs to the technical field of forestry remote sensing and forest resource investigation. The method comprises the following steps: improving data quality through normalization, denoising and thinning processing; fitting a cylinder by using a least square method through the height distribution characteristics of the point cloud to extract a trunk point cloud; an improved mean shift algorithm is combined with adaptive bandwidth adjustment and an abnormal point detection mechanism to perform hierarchical clustering on tree trunk point clouds, a perfect circle is fitted by using a convex envelope algorithm to calculate a sectional area, and a single wood volume is accumulated and estimated by combining a cylinder and cone volume formula. The method solves the problem that a traditional algorithm is difficult in trunk section feature recognition, improves trunk parameter extraction precision and timber volume estimation reliability in a complex environment, supports trunk diameter extraction at any height, is suitable for subtropical man-made forests with different diameters at breast height and canopy density, and has a wide application prospect. And an efficient and accurate technical scheme is provided for forest resource investigation and carbon reserve evaluation.

This invention discloses a method for synergistic optimization of forest stand structure and biomass based on multi-platform LiDAR. Using airborne, vehicle-mounted, and backpack LiDAR point clouds and synchronously measured data, the method first performs denoising, registration, and ground filtering preprocessing. Voxel clustering and region growing algorithms are then used to extract trunk position, diameter at breast height (DBH), tree height, and crown width. Spatial structural units are divided based on a weighted Voronoi diagram of DBH-tree height-crown width grey relational degree. Combining parameter extraction algorithms and species-specific allometric growth equations, six parameters—angular scale (W), DBH-to-size ratio (U), aggregation index (R), openness (K), competition index (CI), and forest layer index (S)—are quantified, and the forest topography index (AGB) of the sample plots is estimated. A complete technical chain is constructed, from data acquisition—structural unit construction—parameter quantification—mechanism analysis—precision management, providing quantifiable support for the sustainable management and carbon sequestration enhancement of mixed coniferous and broadleaf forests.