42 results about "Forest type" patented technology

The invention relates to the technical field of forest image processing, in particular to a forest information extraction method based on a remote sensing image. The method comprises the following steps: obtaining remote sensing image data of a forest area and a GPS positioning sample place area, and carrying out sample area remote sensing image processing and vegetation index analysis to obtain vegetation index data corresponding to each forest type in the sample area; performing end member component reflectivity analysis on the remote sensing image data of the sample area to obtain spectral reflectivity values corresponding to all end member components of the forest in the sample area; performing mixed pixel decomposition on a corresponding sample area remote sensing image in the sample area remote sensing image data in combination with a linear mixed pixel decomposition model so as to generate an abundance map of each ground feature type of the sample area; and carrying out pixel forest information classification extraction on each ground feature type abundance map of the sample region to obtain each forest type distribution map of the sample region. According to the invention, different forest information in the remote sensing image can be obtained by integrating the surface reflectance and the vegetation index.

The invention provides a small-region forest carbon sink accurate estimation method and system based on multiple constraints, and the method comprises the steps: respectively generating a canopy height model, a leaf area index and a chlorophyll content through obtaining unmanned plane laser radar data and multispectral data; generating a digital elevation model by adopting a lowest point filtering technology, realizing individual tree segmentation in combination with a gradient map and a watershed algorithm, and extracting individual tree geometric parameters; and constructing a carbon sink estimation model by using the geometric parameters and the physical and chemical parameters, and comprehensively calculating the carbon sink amount of the small region. The problems that a traditional carbon sink estimation method is insufficient in precision and low in automation level are solved, the single-tree segmentation precision and the carbon sink amount calculation precision are improved through deep fusion of geometric information and spectral information, full-process automation of data acquisition, data processing and carbon sink estimation is achieved, and the method is suitable for large-scale popularization and application. The method is suitable for carbon sink monitoring requirements of various forest types and complex terrain scenes.

The embodiment of the invention provides a forest type remote sensing classification method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a target geographic area and collecting multi-source remote sensing data; extracting canopy multi-dimensional space-time spectrum features based on multi-source remote sensing data, and screening geographical environment covariables capable of representing forest type differences; calculating the overall geographical environment similarity of the unknown point and the sample point by using a Gaussian similarity function; quantifying the individual representativeness of the sample points to the unknown point according to the overall geographical environment similarity, and selecting the forest type represented by the sample point with the highest individual representativeness as the forest type of the unknown point; drawing a forest type spatial distribution diagram and a classification uncertainty distribution diagram of the target geographic area based on classification results of forest types of all unknown points; and performing precision verification on the forest type spatial distribution map based on the classification uncertainty distribution map and a preset precision evaluation index.

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 discloses a forest canopy height remote sensing estimation method based on multi-forest feature fusion and a bidirectional stacking model, and the method comprises the steps: obtaining multi-source remote sensing data of a target region, and employing a multi-stage feature selection method MBF-Control to screen an optimal feature subset for spatial distribution data of different forest types, the multi-source remote sensing data comprises GEDI data, Sentinel-1 / 2 data, Landsat-8 data and DEM (Digital Elevation Model) data; a bidirectional stacking model BS-MFTF of multi-forest type feature fusion is constructed; and inputting the optimal feature subset into the multi-forest feature fused bidirectional stacking model BS-MFTF, and outputting a canopy height estimated value of the target area. Structural diversity and spatial patterns are captured, and canopy height estimation and forest structure mapping are improved.

The invention relates to a forest carbon sequestration ecological function assessment method and device, a storage medium and electronic equipment. A forest carbon sequestration ecological function assessment method belongs to the technical field of ecological environment, and comprises the following steps: inputting meteorological data, soil data, vegetation data and geographic data of a target forest area into an FORCCHN model for setting tree physiological and ecological optimization parameters, obtaining forest net primary productivity of the unit area of the target forest area and forest soil heterotrophic respiration of the unit area of the target forest area; the FORCCHN model is set as tree physiological and ecological optimization parameters corresponding to the forest type of the target forest area; and determining the forest carbon sequestration amount of the unit area of the target forest region based on the forest net primary productivity of the unit area of the target forest region and the forest soil heterotrophic respiration of the unit area of the target forest region, thereby being beneficial to improving the carbon sequestration amount and carbon sequestration function estimation precision of different types of forests.

The present invention discloses a method for estimating forest carbon storage based on artificial intelligence and multi-modal remote sensing data, which relates to the technical field of forest carbon storage estimation. The present invention integrates optical satellite images, lidar data, and hyperspectral image remote sensing data. Through multi-source data fusion, the comprehensiveness and estimation accuracy of the data are improved, and it can more accurately reflect the forest coverage, vertical structure, and biodiversity, overcoming the limitations of a single data source. The entire forest area is divided into multiple sub-regions, and a carbon storage estimation model is separately constructed based on the forest type of each sub-region. The method of estimating area by area refines the carbon storage estimation of different regions, accurately reflects the characteristics of different forest types, and introduces a dynamic carbon storage prediction model based on differential equations. Combining historical and real-time remote sensing data, it simulates the change of carbon storage in the time dimension and captures the impact of factors such as forest growth, climate change, natural disasters, and logging on carbon storage.

The invention discloses a basin hydrological simulation method and system embedded with a canopy interception mechanism, and the method comprises the steps: embedding a revised Gash canopy interception model module in an SWAT model, dividing a rainfall event into a wetting stage, a saturation stage and a drying stage according to the parameters of rainfall, a leaf area index and a tree height, dynamically calculating the canopy interception amount and the interception evaporation amount, and carrying out the calculation of the canopy interception amount and the interception evaporation amount. The effective rainfall capacity is used for replacing original rainfall input in the SWAT model, the adjusting effect of the forest canopy on the rainfall process is reflected more truly, and the basin hydrological simulation precision is improved. According to the method, the response and simulation capability of the SWAT model to the hydrological process of the underlying surface of the forest is effectively improved, the dynamic response to effective rainfall and evaporation terms is enhanced, the method is suitable for watershed hydrological process modeling of various forest types, and the reliability of flood forecasting and water conservation evaluation is improved.

The invention discloses a forest weak disturbance area identification method and device, equipment, a medium and a product, and relates to the technical field of forest detection, and the method comprises the steps: employing a change detection algorithm of a long-time sequence image to construct a prediction model according to a normalized red wave and short wave infrared distance index and a tasseled cap transformation humidity index; constructing a training set according to the two prediction models; each sample in the training set comprises input data and label data, the input data comprises waveform coefficients and environment data of the two prediction models, and the label data comprises a forest type and a non-forest type; the forest type comprises existence of forest weak disturbance and absence of forest weak disturbance; the detection threshold value for distinguishing the existence of the forest weak disturbance and the absence of the forest weak disturbance is obtained by performing adaptive calculation on a prediction model change probability threshold value by utilizing a gradient lifting decision tree algorithm; and training the classification model by adopting the training set to obtain a forest weak disturbance recognition model. The forest weak disturbance detection accuracy is improved.

The invention relates to the technical field of forest resource monitoring, in particular to a forest carbon reserve calculation system and method, and the method comprises the steps: obtaining target data corresponding to a target forest; obtaining forest stand features, climate features and external influence features corresponding to the target forest based on the target data; obtaining forest types, forest stand density, forest stand age groups and soil characteristics based on forest stand characteristics; based on the forest type and the stand density, vegetation carbon reserves are obtained; based on soil characteristics and forest stand age groups, obtaining soil carbon reserves; obtaining a carbon reserve calibration coefficient based on the climate characteristics and the external influence characteristics; and obtaining the forest carbon reserve based on the vegetation carbon reserve, the soil carbon reserve and the carbon reserve calibration coefficient. According to the invention, the calculation precision of the carbon reserves can be improved.

The invention relates to the technical field of biological fireproof forests, and discloses a design method for a biological fireproof barrier forest belt, which comprises the following steps of: 1, designing a biological fireproof forest belt based on the canopy density of 0.2-0.5 of a broadleaf mixed forest by taking the existing forest stand type as the broadleaf mixed forest, screening suitable fireproof tree species by integrating site conditions, natural environment and hydrothermal conditions of the biological fireproof forest belt of the broadleaf mixed forest; 2, cleaning and soil preparation are conducted on a biological fireproof forest belt to form a hole belt-shaped area, comprehensive complementary planting is conducted on fireproof tree species, the complementary planting density of a broad-leaf mixed forest is 2520 plants per kilometer as tree planting points, the fireproof tree species are planted in the hole belt-shaped area through the tree planting points, and the fireproof tree species are planted in the hole belt-shaped area; after planting, a compound fertilizer is used for topdressing, so that the fireproof tree seeds absorb fertility to accelerate the growth speed.

The invention relates to the technical field of forest fire prevention, in particular to a large-scale forest ground surface litter load calculation method. The method comprises the steps of obtaining a forest resource partition to which a target area belongs; determining the forest type of each forest class in the target area based on the national forest resource checking data; according to the forest resource subarea to which the target area belongs and the forest type of each forest class in the target area, establishing an above-ground biomass and stock regression model of each forest class in the target area; acquiring the aboveground biomass of each forest class in the target area according to the aboveground biomass and accumulation regression model; obtaining the land surface litter carrying capacity of each forest class in the target area by inquiring the proportion of the land surface litter carrying capacity in the aboveground biomass in the forest class of each forest type; and obtaining spatial distribution of the surface litter load of the target area according to the surface litter load of all forest classes in the target area. According to the method, the efficiency and accuracy of large-scale forest surface litter load calculation can be effectively improved.

The invention relates to the technical field of forest multi-site registration, and discloses a forest multi-site point cloud registration method and related equipment, and aims to construct a feature grid map by fusing multiple classes of semantic tags, avoid registration failure caused by single feature deletion or poor quality, improve the adaptability in multi-forest, multi-season and multi-shielding scenes, and improve the registration accuracy. A high-stability registration area is selected by means of stability scores, self-adaptive adjustment of registration constraints is achieved in combination with a semantic weighting mechanism, the dependence of coarse registration on an initial pose and artificial experience is reduced, and wrong matching interference is reduced. Through semantic weighting ICP fine registration and global optimization, geometric stability differences of different semantic objects are distinguished, unstable structure interference is reduced, and registration precision is improved. Automatic stable registration can be achieved without manual target, the field operation cost and workload are reduced, and large-scale forest scene popularization is facilitated.

The application relates to the technical field of forest resource monitoring, in particular to a forest carbon storage calculation system and method, which comprises the following steps: obtaining target data corresponding to a target forest; obtaining stand characteristics, climate characteristics and external influence characteristics corresponding to the target forest based on the target data; obtaining forest types, stand densities, stand age groups and soil characteristics based on the stand characteristics; obtaining vegetation carbon storage based on the forest types and the stand densities; obtaining soil carbon storage based on the soil characteristics and the stand age groups; obtaining a carbon storage calibration coefficient based on the climate characteristics and the external influence characteristics; and obtaining forest carbon storage based on the vegetation carbon storage, the soil carbon storage and the carbon storage calibration coefficient. The application helps to improve the calculation accuracy of carbon storage.

The embodiment of the present application provides a method for determining bamboo cultivation target by using the canopy density of upper arbor vegetation type, relates to the technical field of forestry, and evaluates the relevant indexes of the growth of square bamboo planted under different vegetation types in different site conditions, determines the square bamboo cultivation target under the corresponding forest category, forest type and origin of forest in different site conditions according to the evaluation result and the range of the upper arbor canopy that can be adjusted to according to the relevant felling technical regulations, and can also determine the square bamboo cultivation target according to the determined square bamboo cultivation target, and assist in screening the cultivation site by combining factors such as the forest category, forest type and origin, so as to provide a scientific basis for the development planning of bamboo industry.

The invention belongs to the field of biomass high-valued utilization, and provides a method and system for estimating the carbon utilization efficiency of forest biomass high-valued utilization, and the method comprises the steps: determining the initial carbon component content based on the forest type; converting the woods according to a biomass high-valued conversion process, and determining the content of carbon components in the woods after each step of process is completed; constructing a carbon transfer matrix of each step of flow according to the carbon component contents of the input raw materials and the output products of each step of flow; determining the final carbon component content after the forest biomass is subjected to high-valued treatment by utilizing the initial carbon component content and the carbon transfer matrix of all the processes; and determining the high-valued carbon utilization efficiency of the forest biomass based on the ratio of the final carbon component content to the initial carbon component content after the forest biomass is subjected to the high-valued treatment. According to the method, the accuracy of estimating the high-value utilization carbon utilization efficiency of the biomass can be improved, and a reliable measurement index is provided for the technical improvement of the high-value utilization of the biomass.

The invention belongs to the technical field of forest ecological system carbon cycle monitoring, and relates to a regional scale forest net ecological system carbon exchange estimation method fusing ground vortex covariance observation and multi-source remote sensing data. The problems of insufficient representativeness of flux monitoring forest types and limited precision of pure remote sensing estimation during regional scale estimation of forest carbon sink in a traditional method are solved. A membership function matrix is innovatively constructed by using fuzzy mathematics, EC measured data and multi-source remote sensing data are matched, and effective fusion of point and surface data is realized. According to the method, a regional forest carbon sink measurement model based on remote sensing data and a flux observation network is constructed, high-precision regional carbon sink estimation is realized, and single-point limitation is overcome; the spatial pattern (presenting the gradient characteristic of high southeast and low northwest) of the temperate forest carbon sink and the seasonal change are disclosed; it is determined that carbon sink annual fluctuation is mainly influenced by meteorological conditions and vegetation state changes; and an effective tool is provided for accurate evaluation of regional forest carbon sink and carbon management decision.

The application discloses a severe degradation forest restoration force quantitative prediction method and system based on a landscape process, and belongs to the technical field of forest ecological restoration. In view of the problem that the prior art can only provide a qualitative tree species suggestion and ignores the spatial dependence relationship between patches, the application builds a same site condition stable domain quantitative restoration probability, introduces a patch inter-forest type dependence degree matrix to depict the landscape process, and combines a restoration force quantitative prediction model to output a restoration year limit interval of each grid, so that a leap from experience judgment to quantitative probability prediction driven by the landscape process is realized, problems such as inaccurate tree species screening and unreasonable planting density are effectively solved, and the success rate of large-area restoration planning and resource utilization efficiency are significantly improved.

The invention discloses a forest carbon sink parameter time sequence product generation method, system and device and a storage medium, and is applied to the cross technical field of vegetation remote sensing and carbon cycle monitoring, and the method comprises the steps: obtaining multi-source data of a target region, and generating a vegetation index based on remote sensing data in the multi-source data; based on the vegetation index, constructing a nonlinear model for different forest types, and performing inversion to obtain a corresponding net primary productivity initial time sequence; performing type-oriented differential purification treatment on the initial time sequence of the net primary productivity; feature difference identification is carried out based on the purified time sequence, and the nonlinear model is optimized according to an identification result; generating a net primary productivity time sequence product through the optimized model; according to the method, the problems of mechanism mismatching and information loss when a traditional single model processes heterogeneous forest types are solved, and a high-credibility time sequence parameter product basis is provided for urban forest carbon sink accurate monitoring, operation and management policy making and carbon trading market support.

The invention discloses a man-made forest and natural forest deep learning classification method based on a satellite-borne laser radar waveform. The method comprises the following steps: step 1, obtaining an L1B geographic positioning waveform of a satellite-borne full-waveform laser radar GEDI of a forest area; 2, carrying out preprocessing operation on the obtained data to obtain waveform information of high-quality GEDI light spots in an area; step 3, performing dimension raising on the waveform, and encoding the one-dimensional waveform into a two-dimensional matrix format as subsequent input; step 4, constructing a convolutional neural network model, firstly extracting local features of the image through a convolutional layer, reinforcing features of important channels through an SE module, then reducing spatial dimensions of an output feature map through a global average pooling layer, then performing global modeling on the features by using an MHA mechanism, and finally outputting a classification result through a full connection layer; and 5, evaluating the classification precision of the artificial forest and the natural forest. According to the method, two forest types can be distinguished, so that a scientific basis is provided for forest management and ecological resource monitoring.

The application provides a forest resource long-term fixed monitoring sample plot sampling and construction method.The forest resource long-term fixed monitoring sample plot sampling method comprises the following steps: step A1: a regional vegetation distribution map is made on a regional scale by taking a regional map and a boundary, and combining existing forest community distribution data; step A2: the regional map is divided into grids according to the number and density of the monitoring sample plots; and step A3: the number of the monitoring sample plots of each forest type is determined according to the regional vegetation distribution map made in step A1 and the actual needs of scientific research or production work.The forest resource long-term fixed monitoring sample plot sampling and construction method has the advantages that representative fixed monitoring sample plots can be arranged more conveniently, and the goal of accurate forest resource monitoring can be achieved.

The invention relates to the technical field of forest carbon reserve prediction and data screening, in particular to a forest carbon reserve estimation method fusing airborne hyperspectral and LiDAR data. The method comprises the following steps: firstly, determining a forest type, and obtaining a carbon reserve estimation model corresponding to each dominant tree species; aiming at various forests, extracting sample plot shapes, sizes and input data corresponding to the carbon reserve estimation models of the forests; the carbon reserve estimation model extracts key features from the input data of the sample plots, and estimates the carbon reserves of the corresponding sample plots based on the key features; and setting a sample plot with a shape and a size corresponding to the carbon reserve estimation model in each forest type, and obtaining a carbon reserve estimation value of the sample plot through the corresponding carbon reserve estimation model. The method can adapt to different forests and automatically adapt to sample plots, and the problems that when the forest carbon reserve is estimated through the remote sensing technology, a LiDAR single data source is relied on, the topographic relation is complex, and the precision is difficult to guarantee are solved.

The invention discloses a carbon sink measurement method and system for a coastal protection forest, and the method comprises the following steps: obtaining field data of a target coastal protection forest, and carrying out the preprocessing of the field data, and obtaining a three-dimensional point cloud model of the protection forest; based on the three-dimensional point cloud model of the protection forest, adaptive forest structure parameter extraction is carried out, and a vegetation type and a corresponding tree height are obtained; the adaptive forest tree structure parameter extraction comprises parameter adaptive single tree segmentation and segmentation-fitting mutual feedback optimization; and performing carbon sink index calculation based on the vegetation type and the corresponding tree height to obtain the carbon density of the protection forest. According to the invention, by introducing a parameter adaptive initialization and segmentation-fitting mutual feedback optimization mechanism driven by a forest type feature vector, internal self-checking and iterative optimization are realized, and an automatic high-precision carbon sink measurement method adaptively coping with changes of arbors and shrubs in a coastal area is realized.

1.The name of the present design product: gift box (forest type). 2.The use of the present design product: the present design product is used for packaging products. 3.The design points of the present design product: the combination of shape, pattern and color. 4.The picture or photo that best indicates the design points: set 1 open state reference drawing. 5.The design claimed contains color.

The present invention provides a method for remote sensing classification of forest types based on historical sample migration, belonging to the field of forest monitoring. The method first determines the monitoring area, historical years and target year, obtains the corresponding sample points, then obtains the long-term multi-source remote sensing data of the historical sample points and characterizes the forest growth and succession characteristics, then determines whether the forest type has changed, and migrates the sample data that has not changed to the target year; extracts classification features related to remote sensing classification using the multi-source remote sensing data of the target year; at the same time, divides the monitoring area into multiple floristic regions, and selects the optimal classification features for each floristic region from the relevant classification features; finally, classifies the forest types of each floristic region based on the optimal classification features and the migrated historical forestry field survey sample point data that have not changed. The present invention increases the number of sample points in the target year and improves the accuracy of remote sensing classification of mountain forest types under the condition of small samples.

The present application relates to the field of intelligent planting of traditional Chinese medicinal materials and agricultural Internet of Things, and discloses a method for intelligent ecological planting of Morinda officinalis under forest canopy based on environmental monitoring, comprising: S1: determining the target canopy density range based on the forest type, and continuously acquiring the environmental parameters and leaf images of Morinda officinalis growth; S2: implementing ecological improvement of soil under forest canopy and deployment of water regulation infrastructure; S3: identifying and locating the disease spot based on the leaf images, segmenting the disease spot area, extracting the image features and fusing them with the corresponding environmental parameters to construct a multi-source feature set; using the improved NHWOA algorithm to optimize the risk assessment model and output the disease and pest risk grade; S4: making decision based on the real-time environmental parameters and the disease and pest risk grade, generating the regulation instruction and driving the corresponding equipment to execute, and adjusting the decision according to the execution feedback to form a closed loop. The present application realizes real-time sensing and accurate assessment of the microenvironment and disease and pest risk of Morinda officinalis under forest canopy.

The invention relates to the crossing field of traditional Chinese medicinal material intelligent planting and agricultural Internet of Things, and discloses an under-forest intelligent ecological planting method for Cirosa rugosa based on environmental monitoring, which comprises the following steps: S1, determining an adaptive target canopy density range based on a forest stand type, and continuously obtaining environmental parameters and leaf images of growth of Cirosa rugosa; s2, implementing under-forest soil ecological improvement and moisture regulation and control infrastructure deployment; s3, carrying out pest and disease damage identification based on the leaf image and positioning disease spots; segmenting a scab region, extracting image features of the scab region, fusing the image features with corresponding environment parameters, and constructing a multi-source feature set; optimizing the risk assessment model by using an improved NHWOA algorithm, and outputting a disease and pest risk level; and S4, performing decision judgment based on the real-time environment parameters and the disease and insect pest risk levels, generating a regulation and control instruction, driving corresponding equipment to execute, and adjusting a decision according to execution feedback to form a closed loop. According to the invention, real-time perception and accurate evaluation of microenvironment and pest and disease damage risks under the arrowhead forest are realized.

The application discloses a forest remote sensing image classification method and system based on a convolutional neural network, and relates to the technical field of remote sensing image processing. The method steps comprise the following steps: S1, a de-fogging algorithm is used to remove thin clouds and fog in a remote sensing image, and the brightness of the remote sensing image is enhanced; S2, the edge contour of a thick cloud layer shielding area in the remote sensing image is detected, and an initial mask of the thick cloud layer shielding area in the remote sensing image is obtained; S3, the thick cloud layer shielding area in the remote sensing image is replaced through global search matching, and the restoration of a ground object in the remote sensing image that is shielded by the thick cloud layer is realized; and S4, a convolutional neural network is used to extract features of the restored remote sensing image, and the detection and classification of forest types and pest conditions in the remote sensing image are realized. The application realizes the accurate detection of forest types and pest conditions by using high spatial resolution images in combination with a convolutional neural network.

The invention belongs to the technical field of forest remote sensing, and discloses a P-band SAR polarization characteristic-based forest type identification method, which comprises the following steps of: solving a coherence matrix and a covariance matrix on the basis of a pre-processed polarization scattering matrix, and carrying out wavelength dependence correction on basic radar statistical characteristics for a forest three-layer vertical superposition scattering structure formed by a P band; extracting feature subsets of a scattering mechanism related to a forest vertical structure, integrating the corrected basic features and adding polarization phase difference and scattering mechanism combination coefficient feature dimensions to form a complete polarization feature set covering multiple types of features, and extracting various terrain parameters of a target forest region based on DEM data to obtain a complete polarization feature set covering multiple types of features; performing terrain sensitivity analysis on the preliminarily screened feature subset and dividing sensitivity grades, constructing an adaptive terrain correction model in combination with P-band under-forest surface scattering proportion characteristics, and introducing a radar wave polarization state correction term to complete feature radiation normalization correction.

This invention discloses a collaborative inversion method for multiple attributes of individual trees based on transfer learning, relating to the field of forest ecological remote sensing monitoring technology. The method acquires remote sensing images and training samples, generates a canopy height model based on LiDAR point clouds, constructs and trains a single-tree segmentation model based on the U-Net++ architecture, and fine-tunes the encoder weights of the single-tree segmentation model by transferring them to a tree height prediction model and a forest type classification model. Finally, the remote sensing images are input into the three models to obtain the single-tree canopy boundary, predicted tree height, and forest type, and the integrated output is the multi-attribute inversion result for individual trees. This method addresses the problems of complex data dependencies, non-reusable features, low inversion accuracy and efficiency, and easy error accumulation inherent in traditional independent inversion of single-tree attributes.