369 results about "Forest resource" patented technology

The present invention relates to the technical field of terrestrial laser scanning-based forestry data processing, and disclosed are a broad-leaved forest individual tree segmentation method and system based on branch and trunk information guidance. The method comprises: taking a terrestrial laser scanning-based broad-leaved forest point cloud as a processing object, and using RANSAC cylinder fitting in combination with growth characteristics of a tree trunk to perform tree trunk detection; extracting, starting from the top of the tree trunk in a low-vegetation area, tree branches and trunk by means of segmented growth of the branches and trunk in combination with changes in the thickness of branch and trunk segments; and segmenting, starting from the tips of the branches, a tree crown leaf point cloud by means of layer-by-layer growth. The present invention achieves a stronger tree trunk detection capability under the conditions of lush low vegetation and complex terrain, and moreover, the present invention can achieve accurate segmentation of tree crowns when large and small tree crowns are intertwined and multiple tree crowns are closely surrounded. The algorithm is highly efficient and easy to use, and has great practical significance for improving the semantic understanding capability of forest scenes, and assisting in forest resource survey, vegetation ecology research, and calibration and verification of satellite remote sensing products.

The invention discloses a multi-source business data collaborative acquisition method for subcompartment change, and relates to the technical field of forest resource management, and the method comprises the following steps: S1, capturing subcompartment boundary, ownership and operation attribute change events in real time through a dynamic topology perception layer, and generating a dynamic topology map; according to the multi-source business data collaborative acquisition method oriented to the subcompartment change, a dynamic topology sensing mechanism and a standardized acquisition process are constructed, so that the problem of multi-source data splitting caused by frequent subcompartment change is solved, and real-time collaborative acquisition and consistency guarantee of multi-source heterogeneous data are realized. A dynamic topological graph accurately senses boundary adjustment and ownership change events, and a double-channel analyzer is combined to automatically map service rules into equipment instructions, so that the data association error rate is remarkably reduced; the bimodal verification mechanism effectively eliminates version conflicts and business rule contradictions through time sequence alignment and sandbox simulation, and improves the accuracy of forest resource assessment.

The invention provides an intelligent forest fire prediction and early warning system which integrates satellite remote sensing, unmanned aerial vehicle patrol, ground monitoring and other technologies and intelligently identifies ecological behaviors by using a deep learning algorithm. The system is composed of four core modules: a multi-source data acquisition module, an ecological behavior identification module, a machine learning prediction model module and an intelligent early warning and emergency response module. The forest environment is comprehensively monitored, ecological data is analyzed, the fire risk is accurately predicted, early warning information is automatically generated, and the system is closely linked with an emergency system. The system effectively solves the problems of single traditional early warning means, poor timeliness and insufficient precision, significantly improves the accuracy and response speed of forest fire early warning, and provides powerful technical support for forest resource protection and ecological environment safety.

The invention relates to the technical field of remote sensing surveying and mapping, in particular to a forestry surveying and mapping method based on remote sensing big data, which comprises the following steps: integrating remote sensing images, unmanned aerial vehicle images, temperature sensor data and humidity sensor data to perform space-time alignment processing, filling missing data through time interpolation, and performing abnormal value detection at the same time; and combining the processed data to calculate ecological indexes of the forest area, and outputting a forest health ecological assessment result. According to the method, by integrating remote sensing images, unmanned aerial vehicle images and temperature and humidity sensor data, the accuracy and integrity of data are improved, species identification and ecological function contribution analysis are achieved, key species and contribution of the key species to an ecological system can be accurately identified, and by analyzing the dynamic change trend of forest resources and climate influence, the forest resources can be accurately identified. According to the method, resource configuration can be effectively optimized, the management efficiency and the risk management and control capability are improved, the precision and timeliness of forest resource monitoring, assessment and management are improved, and the sustainable utilization and ecological protection capabilities are enhanced.

The invention provides a forest fire spreading simulation method and system based on an unmanned aerial vehicle LiDAR technology, and the method comprises the steps: collecting forest region data based on the unmanned aerial vehicle LiDAR technology, a camera and a sensor; wherein the forest area data comprises LiDAR point cloud data, thermal infrared data, image data and meteorological data; based on the forest region data, performing forest region feature extraction to obtain feature parameters; based on the characteristic parameters, constructing a fire spread dynamic model through a fire spread dynamics reinforcement learning framework and a vertical-horizontal fire spread coupling algorithm; based on the fire spreading dynamic model, fire spreading simulation and prediction are carried out, dynamic early warning is carried out based on simulation and prediction results, and a fire fighting and fire prevention decision is obtained. The technical scheme of the invention has important significance for protecting forest resources and maintaining ecological balance.

The invention relates to a tree modeling method based on image acquisition and image recognition of an unmanned aerial vehicle, and the method comprises the steps: planning a flight path of the unmanned aerial vehicle according to the scale of a target region and tree distribution, and collecting a multi-angle image of a tree; then, denoising, cutting and enhancing the image through preprocessing, and performing semantic segmentation on the image by using a pre-trained neural network to obtain a semantic tag of each pixel; image feature points are extracted through an SIFT algorithm and are matched, and a preliminary three-dimensional point cloud model is constructed by using a COLMAP incremental reconstruction method; according to the semantic segmentation result of the image, the semantic label of each pixel is transmitted to the point cloud data, and the semantic annotation of the point cloud is completed; traversing the point cloud data, and segmenting the point cloud according to the label to obtain a tree three-dimensional point cloud model; the method can be widely applied to the fields of vegetation monitoring, forest resource investigation, ecological environment protection and the like, and has the advantages of high precision, automation and high efficiency.

The invention relates to the technical field of data management, in particular to an intelligent forestry data management system and method based on data fusion. The method comprises the following steps: obtaining forest region sensing data and forest region remote sensing data, and carrying out forest region resource trend analysis to obtain forest region resource trend data; constructing a seasonal forest region resource state model set based on the forest region grid resource state data and the forest region resource trend data; performing forest tree suitable region division according to the seasonal forest region resource state model set to obtain forest tree suitable region data; performing forest growth migration factor calculation according to the seasonal forest region resource state model set to obtain a forest growth migration factor; and performing forest region forest growth health degree evaluation based on the forest growth offset factor to obtain forest region forest growth health degree data, and performing forest region planting strategy optimization to obtain a forest region planting management strategy. The automation level of forest resource management can be improved.

The invention relates to the technical field of satellite-borne hyperspectral image processing and analysis, and solves the technical problem that the huge spectral and spatial resolution difference between low-resolution hyperspectral data and high-resolution multispectral data cannot be fully considered in the existing method. The forest hyperspectral satellite image-oriented deep and shallow double-branch super-resolution method comprises the steps of constructing a double-branch network architecture, performing feature fusion reconstruction on output features of the double-branch network architecture, and obtaining high-resolution hyperspectral data with low spectral variation characteristics of forest vegetation in spaceborne hyperspectral image data. According to the method, the problem of modal difference between low-resolution hyperspectral data and high-resolution multispectral data is effectively solved by learning on different feature levels and scales, and the model is enabled to pay more attention to low-spectral variation characteristics of different forest vegetation in a satellite image through a plurality of feature attention mechanisms. And the requirements of subsequent fine monitoring tasks of various forest resources can be met.

The invention relates to the field of forest resource monitoring, and discloses a large-scale forest canopy height inversion method based on ICESat-2 and multi-source remote sensing data, and the method comprises the following steps: S1, collecting multi-source data; s2, carrying out data preprocessing and resampling; s3, multi-temporal data of Sentinel-2 are analyzed, a key phenological period is determined, normalized spectral features are developed, and vegetation is classified; s4, constructing a response canopy height of an explanatory variable and ICESat-2 ATL08, and training a forest canopy height regression model based on forest features based on a random forest regression algorithm; and S5, performing spatial inversion on the continuous interpretation variables by using the trained model, and generating a large-scale forest canopy height map. According to the method, discrete height data of ICESat-2, Sentinel-1 / 2 and auxiliary data are combined, and the forest canopy height is accurately inverted by adopting a random forest regression algorithm.

The invention discloses a forest quality evaluation method based on big data analysis, and relates to the field of forest quality evaluation, and the forest quality evaluation method based on big data analysis comprises a data acquisition analysis module, a rating index selection module, a research analysis module, a forest quality evaluation index module and a result analysis module. According to the method, analysis can be carried out through big data, and a complex forest evaluation problem can be refined into a plurality of factors at different levels, so that comprehensive and systematic evaluation of forest quality is realized, forest conditions can be mastered more accurately, powerful support can be provided for forest operation and management, and the method is worthy of popularization and application. Meanwhile, accurate data of all forest parks can be obtained, the forest quality is estimated, a scientific forest management strategy is made, sustainable utilization of forest resources is promoted, a scientific and reasonable forest quality evaluation system is constructed, and the basis of the accurate improvement effect of the forest quality can be measured.

The invention relates to the technical field of computers and the technical field of intelligent monitoring of forest resources, and particularly discloses a multi-level forest resource monitoring method and system. The method comprises the following steps: collecting computing power of edge nodes, storing and communicating state information, performing normalization processing, and constructing task resource mapping in combination with a forest resource monitoring task set; constructing a scheduling optimization model based on the mapping, executing multi-constraint scoring and iterative optimization, and generating a task-node matching structure; compressing the central graph neural network model according to a matching result and deploying the central graph neural network model to edge nodes; selecting a communication mode in combination with the communication state, and configuring channel resources; executing local reasoning and asynchronously returning data; and finally, extracting features to complete data aggregation, feeding back task weights and node scores, and updating a scheduling strategy in a closed loop. According to the method, the global monitoring scheduling efficiency and the system stability under the environment of uneven resource distribution and communication fluctuation are effectively improved.

The invention provides a natural resource-forest ecology-social economic system coupling coordination management mode, which belongs to the field of resource management and comprises the following steps: (1) selection of a research area: selecting a Yellow River basin as the research area according to economic development, forest resources and population conditions; (2) constructing an evaluation index system of a natural resource-forest ecology-social economy system by using an Entropy Evaluation Method (EEM), which comprises the steps of establishing a forest ecology function subsystem, a natural resource subsystem and a social economy subsystem; and (3) through a coupling coordination degree model (CCDM) and an obstacle diagnosis model (ODM), the coupling coordination relationship of the natural resource-forest ecology-social economy system and the influence factors of the coupling coordination relationship are measured, and the forest ecology-social economy system and the influence factors of the forest ecology-social economy system are measured through the coupling coordination degree model (CCDM) and the obstacle diagnosis model (ODM). According to the research, a feasible path can be provided for differential management of the drainage basin, and protection and sustainable development of a drainage basin ecological system are promoted.

The invention relates to the field of data fusion processing, and particularly discloses a data fusion method of a forestry data management system, which is used for processing real-time acquired data of the forestry data management system. Dividing the forest resource data of the target area into different independent areas, and establishing a special forest resource growth quantitative model for the forest resource data of each independent area; detecting whether a forest resource data conflict exists or not; when the forest resource data of a certain independent region conflicts, the conflict data is corrected through the forest resource growth quantification model of the independent region; when the forest resource data of a plurality of independent regions conflicts, the conflict data is corrected through the combination of the forest resource growth quantification models of the plurality of independent regions; and correcting the conflict data and then completing data fusion.

The invention provides a picea flower monochamus alternatus attractant and a use method thereof.The attractant comprises plant source pheromone and insect pheromone, the chemical components are mixed according to a certain dosage and proportion and then dropwise added to slow-release carriers such as a slow-release bottle, a slow-release bag and a capillary tube, and a picea flower monochamus alternatus lure is prepared; the insecticidal composition can be used for trapping a large number of black longhorn beetles of picea flowers and has the characteristics of remarkable trapping effect and longer lasting period. The attractant is simple to prepare, good in attraction effect and not prone to generating resistance, the formula is a combination of host plant volatile matter and insect pheromone, the attractant is harmless to people and livestock and free of pollution to the environment, a practical pollution-free monitoring and prevention technical means is provided for large-scale trapping and killing of the black longhorn beetles in forests, forest resources are protected, and the economic benefits are increased. The ecological balance is maintained, the application value is very good, and the sustainable development of forestry is promoted.

The invention provides a forest accumulation monitoring analysis method and system based on a remote sensing technology. The forest accumulation monitoring and analyzing method based on the remote sensing technology comprises the following steps: acquiring a forest image through an unmanned aerial vehicle; fusing the forest images to generate a fused image; enhancing the fused image to generate an enhanced image, and extracting forest features and spatial features in the enhanced image; according to the forest features and the spatial features, accumulation parameters corresponding to the forest district are determined; and comparing the accumulation parameters with historical accumulation parameters to generate a monitoring analysis report. In the process, an unmanned aerial vehicle is used for efficiently collecting forest images, key forest features and spatial features are extracted through an image fusion and enhancement technology so as to accurately calculate accumulation parameters of an area, the accumulation parameters are compared with historical data, and a detailed monitoring analysis report is generated. The accuracy and real-time performance of forest resource management are improved, and a scientific basis is provided for ecological protection and sustainable development.

The invention discloses a forest resource investigation method based on a laser radar and visible light synchronous photography technology, and belongs to the technical field of forestry. The method comprises the following steps: selecting a forest stand to be measured, and setting a scanning route; scanning the forest stand by using a laser radar and visible light synchronous photography technology; in the forest stand model, directly identifying tree species, and counting forest stand density; exporting point cloud data, performing individual tree segmentation according to a forest tree sequence, and generating DBH data and position coordinate data in batches; calculating the stand volume and tree species composition of the forest stand; determining a space structure of the forest stand according to the diameter at breast height data and the position coordinate data of the forest, and calculating a competition index of each tree; and linking the database with the forest stand model, establishing an augmented reality forest stand model, and realizing visualization of the forest resource data model. According to the method, the three-dimensional visual model of the forest stand can be established, the tree species can be directly identified, the forest resource survey data can be obtained through calculation, the survey efficiency is high, the cost is low, the data storage amount is small, and the later data processing difficulty is low.

The invention discloses a forest patrol method and device based on an unmanned aerial vehicle, electronic equipment and a medium, and relates to the technical field of forest resource management. The unmanned aerial vehicle carrying a laser radar and image acquisition equipment is controlled to acquire initial laser radar data and initial image data according to a preset flight route; the three-dimensional perception of the complex three-dimensional environment of the forest is realized, and the concealment abnormity is effectively captured. Performing data preprocessing on the initial laser radar data and the initial image data to generate target laser radar data and target image data; and carrying out feature fusion on the target laser radar data and the target image data. Through data preprocessing and multi-source data fusion analysis, the precision of capturing subtle anomalies is improved, and the limitation of a single sensing means is overcome. And finally, forest anomaly recognition is performed based on the fusion features, the geographic information system data and the multi-source sensor data, so that the comprehensive sensing capability of forest anomaly is comprehensively improved, and the forest patrol effect is optimized.

The present invention discloses a method for detecting forest disturbances based on a full-polarization SAR image, which includes obtaining a full-polarization SAR image within a target area, performing image classification for the reference time phase and the monitoring time phase, performing pre-processing including introduction of the SAR image, polarization matrix transformation of the SAR image, Refined Lee filtering, and false-color synthesis on the classified full-polarization SAR image to obtain a false-color synthesized SAR image, inputting the false-color synthesized SAR image into an optimized surface change detection model to extract surface change patches, combining with a radar vegetation index RVI image to obtain initial forest disturbance patches, performing an overlay check on the initial forest disturbance patches to obtain forest disturbance patches within the target area, and generating a visualization result. It has efficient, accurate, and reliable detection capabilities, can observe the ground all day long, all-weather, and penetrate clouds and fog, can be widely applied in fields such as forest resource monitoring and environmental protection, and can better serve ecological protection restoration supervision and management.

The invention relates to the technical field of forest data management, in particular to a forest resource management platform based on satellite data, which comprises a data acquisition module, a data processing module, an emergency early warning module and a personal APP module, and is characterized in that the data processing module divides forest resources into elderly ancient wood resources and general forest resources. A first-level core protection area and a second-level buffer area are respectively set up for different old ancient trees, indexes such as forest stand volume V, forest stand canopy density C and tree density D are accurately measured and calculated for general tree resources, a rotation area and a logging area are scientifically planned according to the indexes, a dynamic monitoring and evaluation mechanism is established, sustainable utilization of forest resources is ensured, and the forest resource utilization rate is increased. Refining and scientization of forest resource management are realized.

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 provides a forest resource dynamic supervision method based on a multi-temporal laser radar, and the method comprises the steps: carrying out the data collection of a target forest in different periods, and obtaining multi-temporal point cloud data, which is used for monitoring the change of the target forest in a time sequence; performing hierarchical processing on point cloud of each period in the multi-temporal point cloud data by taking an echo sequence and echo intensity as constraint conditions to obtain canopy layer point cloud data and ground layer point cloud data, calculating a crown change index and a ground change index, setting a crown change threshold value and a ground change threshold value, and calculating a crown change threshold value and a ground change threshold value according to the crown change index and the ground change index; the method comprises the following steps: identifying regions with significant change of crowns and grounds, realizing classification of crown and ground changes, obtaining crown and ground change analysis results, and drawing a forest resource change distribution map. According to the method, quantitative analysis is carried out by constructing a plurality of change indexes, automatic extraction, classification and mapping of forest crown and ground change information are completed in combination with change threshold segmentation, and forest resource changes are dynamically monitored.

The invention discloses a portable surveying and mapping device for forest resource investigation. The portable surveying and mapping device comprises a storage box, a surveying and mapping instrument and a bracket, a surveying instrument storage cavity and a bracket storage cavity are sequentially formed in the storage box from top to bottom; the upper portion of the surveying instrument storage cavity is of an opening structure, a lifting table and a lifting driving mechanism used for driving the lifting table to do linear motion are arranged in the surveying instrument storage cavity in a sliding mode in the vertical direction, and the surveying instrument is fixedly arranged on the upper portion of the lifting table; the support comprises a mounting plate, a supporting assembly is arranged on one side of the mounting plate, and a connecting assembly is arranged on the other side of the mounting plate. The device is very convenient to assemble and disassemble, can be quickly unfolded and stored during field operation in a forest area, and effectively improves the surveying and mapping operation efficiency during forest resource investigation.

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 management data processing method driven by a large language model, and belongs to the technical field of forest resource management and artificial intelligence data processing crossing. The method comprises the steps of multi-source data acquisition and preprocessing, forest management knowledge graph construction, large language model fine adjustment and retrieval enhancement generation, data semantic fusion and understanding and management strategy reasoning, wherein the large language model performs automatic reasoning based on fused data, management intention and industry knowledge to form a management strategy conforming to a forest growth law and multi-target balance; operation plan text generation: on the basis of completing strategy and space matching, compiling a forest operation plan text meeting forestry industry specifications and management requirements, and man-machine interaction and continuous optimization: collecting feedback information of forestry workers on forest operation plans through a man-machine interaction interface, and systematic evaluation is carried out on the generated operation scheme from forest resource sustainability, ecological function improvement, operation goal achievement degree, risk controllability and policy compliance.

The invention specifically relates to a forest stock dynamic exploration system and method based on unmanned aerial vehicle multispectral fusion, and relates to the technical field of forest resource monitoring. A data preprocessing module; a feature extraction module; a model construction and estimation module; and a result output and visualization module. According to the method, a multi-dimensional data fusion and dynamic calibration mechanism changes the limitation of a traditional fixed soil regulation coefficient; when the soil regulation coefficient is obtained, four core factors of soil type, terrain illumination, soil humidity and texture partition distance are comprehensively considered; interference of different soil backgrounds, terrain illumination and humidity changes on vegetation index calculation is effectively eliminated, the enhanced vegetation index can reflect the coverage and health condition of forest vegetation more truly, a high-precision data basis is provided for subsequent forest stock estimation based on the vegetation index, and compared with a traditional method, the method has the advantage that the method is more accurate. And the data accuracy and reliability are improved.

The invention discloses a tree species identification method based on a bark image and a lightweight deep learning model, and belongs to the crossing field of computer vision and forestry information technology, and the method comprises the steps: S1, collecting an image of a tree bark area; s2, preprocessing the collected data, and positioning and cutting a bark area based on an image segmentation technology; s3, using an improved lightweight deep learning model of a ConvNeXtV2 framework to carry out feature extraction on the preprocessed bark image; s4, after feature extraction is completed, features are integrated by using an adaptive feature fusion strategy, then tree species are classified, and a tree species identification result is output; s5, performing multi-dimensional verification and visual analysis on the model performance; according to the method, the calculation complexity is remarkably reduced, the generalization ability and stability of the model are kept, and reliable technical support is provided for practical application such as ecological monitoring, forest resource management and intelligent forestry.

The invention discloses a forest degeneration degree, degeneration type and degeneration process identification method, and relates to the field of forest resource monitoring and ecological environment evaluation.The method comprises the steps that multi-source data obtained in a research area is preprocessed, and the preprocessed multi-source data is determined; selecting multi-dimensional indexes from three aspects of forest degradation structure, composition and function, and constructing a multi-dimensional index system according to the preprocessed multi-source data; the multi-dimensional indexes comprise forest coverage rate, crushing degree, tree variety diversity, aboveground biomass and net primary productivity; generating a forest degradation index according to each index in the multi-dimensional index system; determining a forest degeneration degree according to the forest degeneration index, generating a forest degeneration type graph in combination with land coverage data to reveal degeneration differences of different forest types, analyzing a dynamic change track of a forest degeneration region in combination with a normalized combustion index time sequence, and identifying a forest degeneration process; according to the method, the multi-dimensional characteristics of degradation can be comprehensively revealed.

The invention provides a multi-source remote sensing data fusion forest resource management method and system, and the method comprises the steps: extracting forest dynamic change features from cross-modal multi-source remote sensing historical data, generating a time sequence correlation curve between cross-modal data, and determining a cross-modal time sequence deviation value between different remote sensing modals through the time sequence correlation curve; calculating the characteristic coincidence degree of different remote sensing modes under the normalized spatial scale for the real-time multi-source remote sensing data, and determining the spatial scale adaptation degree of the real-time multi-source remote sensing data according to all the characteristic coincidence degrees; the fusion feature confidence of the real-time multi-source remote sensing data is calculated through the cross-modal time sequence deviation value and the spatial scale adaptation degree; and generating regionalized management parameters of forest resources of the target forest region based on the fusion feature confidence, and performing resource level-to-level management on the target forest region according to the regionalized management parameters. By adopting the scheme, precise forest resource level-to-level management can be realized based on time consistency and space consistency of multi-source remote sensing data fusion.

The invention relates to the technical field of forest resource analysis, in particular to a forest resource data analysis method and system based on forestry ecological data. The method comprises the following steps: performing altitude measurement on a target detection area to obtain an altitude measurement data set; performing landform classification on the target detection region according to the altitude measurement data set to obtain landform type data; performing ecological partition on the target detection region according to the landform type data to obtain ecological partition data; performing forestry ecological data acquisition on the target detection region according to the ecological partition data to obtain a forestry ecological data set; performing vegetation diversity determination on the target detection region based on the forestry ecological data set to obtain vegetation type distribution data; and performing land type-vegetation planting integrating degree determination on the target detection region according to the vegetation type distribution data to obtain a land-vegetation planting integrating degree set. According to the method, the efficiency and accuracy of forest resource data analysis are remarkably improved.

The invention relates to the technical field of forest resource investigation and point cloud processing, and discloses a laser radar individual tree segmentation method based on point cloud dimensionality reduction and individual tree network reforming, and the method comprises the steps: taking airborne LiDAR point cloud as a special network structure, replacing a single point with a point cloud cluster, and taking a fitting cluster circle as a hub. Dimensionality reduction processing from the three-dimensional point cloud to the two-dimensional hub is carried out, and redundant information is reduced. Three hub clustering methods (Euclidean distance, four-dimensional similarity and cosine similarity) are provided to construct individual tree hub clusters, and clustering results are mapped back to the three-dimensional point cloud to realize individual tree segmentation. The calculation pressure caused by huge point clouds is effectively relieved, and meanwhile, the segmentation precision and universality are improved; through a multi-strategy hub clustering method, the adaptability and robustness in a complex forest environment are improved; by introducing automatic precision evaluation based on convex hulls, low efficiency and subjectivity of traditional manual discrimination are avoided, objectification and automation of segmentation precision are realized, and the method has high theoretical value and application prospect.