27 results about "Forest structure" patented technology

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 provides a forest structure change detection method, a related device and a computer storage medium, and the method comprises the steps: firstly generating target point cloud data based on original laser radar point cloud and multi-source fusion positioning data of a target forest region; determining the current canopy entropy of the target point cloud data based on the spatial distribution of the target point cloud data; and finally, based on the current canopy entropy and the reference canopy entropy, generating a change detection result of the forest structure. And the purpose of rapidly and accurately detecting the forest structure change is achieved.

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

The application discloses a forest structure and intelligent coupling analysis and modeling method for absorbing photosynthetically active radiation, and belongs to the technical field of forest ecology remote sensing, structure analysis and computer simulation. The method focuses on fusing airborne and handheld laser radar information, accurately inverting forest structure indexes, covering single-tree level attributes, neighborhood spatial structure indexes, gap structure, stand density SD and slope SL; three-dimensional radiation transmission simulation is carried out; through the construction of a single-tree three-dimensional model library and a digital sample plot, high-precision three-dimensional forest reconstruction and hierarchical APAR simulation are realized, and photosynthetic spatial heterogeneity parameters are extracted; Spearman correlation analysis is carried out based on the structure and APAR parameters, and PCA dimension reduction and KMeans clustering are combined to identify the dominant structure mode and spatial type; based on the clustering result, a typed structure-APAR coupling integrated learning model is constructed, and the regulation mechanism of single-tree attributes and neighborhood structure on APAR is revealed.

The application provides a polarization SAR image simulation method for forests under complex terrain conditions, comprising the following steps: (1) setting parameters of a polarization SAR imaging process, including radar imaging parameters, forest parameters and terrain parameters; (2) generating a corresponding terrain model according to the terrain parameters; (3) constructing a forest model according to the forest parameters; (4) calculating a polarization SAR image according to the forest parameter model, and finally simulating the polarization SAR image of the forest under the complex terrain conditions. The method can improve the simulation of the polarization SAR image of the forest under the complex terrain conditions, can be used for understanding the remote sensing mechanism, testing new applications and designing the inversion algorithm of the forest structure, and can further explore the influence of the terrain on the polarization SAR image imaging process.

The application discloses a kind of visual Token compression method and device of multi-modal large language model based on space-time forest, comprising: each frame video frame is input into multi-modal large language model, after the visual encoder in multi-modal large language model, the visual Token embedding sequence corresponding to each frame video frame is obtained, from the visual Token embedding sequence corresponding to each frame video frame, several key visual Token embeddings are selected and node feature is constructed;Node feature is input into space-time forest algorithm, each key visual Token embedding is regarded as node, the directed edge between two nodes is constructed based on the joint constraint of semantic similarity, spatial distance and time sequence, node is constructed into space-time tree by directed edge and is organized into space-time forest structure, and space-time forest structure is processed under preset constraint, obtain final node set, the key visual Token embedding corresponding to the node in final node set is regarded as compressed visual Token embedding sequence.The application can reduce the amount of calculation of model.

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

The application discloses a method for setting forest monitoring standard plots based on airborne laser radar data, comprising the following steps: S1: airborne laser radar surveying and mapping is carried out on the survey monitoring target forest stand; S2: surveying and mapping laser radar point cloud achievement data are acquired; S3: point cloud data are preprocessed; S4: a grid effective layout range is identified; S5: grid layout and effective grid screening are carried out; S6: forest structure parameter mean value extraction and high density index calculation are carried out; S7: effective grid layering is carried out; S8: HDI distribution is calculated layer by layer, and index concentrated distribution range grid is extracted; S9: typical grid screening is carried out; and S10: standard compliance verification and final determination are carried out. The method provided by the application can accurately extract forest stand factor information, and can provide standard plot settable plots according to standard plot setting principles, and can extract terrain factors of the plots, so that the typicality of the plots can be ensured, and the uncertainty brought by complex terrain to forest survey work can be avoided.

The application uses images to construct a forest sample plot measurement model. The three-dimensional reconstruction of images obtained by using a smart phone has the advantages of low measurement cost and simple actual operation suitable for field operation. The improved DBSCAN algorithm improves the accuracy of clustering, so that the constructed three-dimensional measurement model can be used for obtaining forest structure parameters, and the measurement accuracy of the structure parameters is much higher than the national forest second-class survey standard.

The invention discloses an ICESat-2 adaptive photon denoising method and an ICESat-2 adaptive photon denoising system based on vegetation layering. According to the method, a vegetation layering strategy is introduced, the photon data is divided into different gradient grades according to the vegetation coverage, and the problem that a traditional global denoising method is insufficient in adaptability in a complex vegetation environment is effectively solved. And the optimal denoising parameter of each vegetation level is adaptively determined in combination with the Bayesian optimization algorithm, so that the photon denoising precision and efficiency of the DBSCAN clustering in the vegetation region with uneven density are remarkably improved. According to the method, the advantages of multi-source remote sensing data are fully utilized, efficient screening and separation of signal photons and noise under different vegetation density conditions are achieved, and a higher-quality data basis is provided for subsequent forest structure parameter inversion and ground feature classification.

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.

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.

The invention relates to a discontinuous microscopic image automatic splicing method based on multi-seed region growth and global layout, which comprises the following steps: acquiring a microscopic image sequence set to be spliced and preprocessing the microscopic image sequence set, processing the preprocessed image based on a multi-seed region growth method to form an associated forest structure, and carrying out local and global optimization on the associated forest structure, and generating and outputting a result. According to the scheme of the invention, an image set is intelligently divided into a plurality of connected sub-graphs (forest structures), local and global optimization is carried out on the connected sub-graphs, and global coordinate optimization of sub-graph nodes is carried out on adjacent nodes crossing the connected sub-graphs based on prior expected relative displacement. And carrying out splicing and coordinate optimization on the internal and global macroscopic layouts of each sub-graph, inhibiting error accumulation, and realizing high-precision splicing.

The invention relates to the technical field of forest remote sensing and forest structures, in particular to a forest window extraction and analysis method based on GF-2 images and object-oriented machine learning, which comprises the following steps: S1, determining a research area and collecting data; s2, data preprocessing; s3, constructing an object-oriented multi-level structure segmentation classification model; s4, forest window extraction based on an object-oriented multi-layer structure segmentation classification model; s5, exploring forest window and vegetation index response; and S6, estimating long-time-sequence forest window evolution. Through GF-2 image high-precision forest window extraction, feature statistics, spatial pattern analysis and forest window and vegetation index response modeling, the problems of low forest window identification efficiency, incomplete analysis, difficult dynamic monitoring and insufficient vegetation relationship quantification are solved, and support is provided for dynamic monitoring and scientific management of a forest ecosystem.

The invention provides a forest structure parameter inversion model obtaining method, a forest structure parameter inversion model using method and related equipment, and is applied to the technical field of artificial intelligence. According to the method, a training set containing space lag variables is constructed based on actual measurement data of current environment factors and forest structure parameters of target area samples; a neighborhood sample is determined through spatial neighborhood matching, nonlinear mapping is performed by using a multi-layer perceptron (MLP) network structure based on a spatial Durbin model, a gradient descent optimization model is adopted, a spatial autocorrelation effect is effectively considered, spatial dependence among pixels is eliminated, and the precision of forest structure parameter inversion is effectively improved.

The invention relates to the technical field of remote sensing image processing, in particular to a forest structure remote sensing parameter-based post-fire recovery process evaluation method, which specifically comprises the following steps of: S1, recognition and severity recognition of burned patches: setting a research area, selecting a burned area and a non-burned area in the research area, taking Landsat time sequence data as a source, and carrying out evaluation on the severity of the burned patches; after preprocessing, synthesizing a Landsat time sequence stack and calculating an NBR time sequence, then using a LandTrendr algorithm to obtain NBR values before and after occurrence of a forest disturbance pixel, and combining an RdNBR index to distinguish the severity degree of the burned plaque into a low grade, a medium grade and a high grade; s2, forest structure parameter remote sensing inversion; and S3, reconstructing the post-firing recovery process. According to the method, the dNBR is introduced to serve as a predictive variable, and the defect of an existing remote sensing inversion model in the aspect of fire disturbance intensity characterization is effectively overcome by enhancing the response capacity of the forest structure parameter remote sensing inversion model to the fire disturbance intensity.

The present application relates to the technical field of three-dimensional reconstruction, and proposes a forest structure reconstruction system based on three-dimensional laser scanning point cloud modeling. The system acquires forest point cloud data through multi-source laser scanning, and completes data preprocessing in combination with auxiliary perception information; on this basis, a hierarchical feature expression mode is constructed, a three-constraint joint mechanism and a multi-source feature balance mechanism are introduced, and the accurate separation of tree trunks, branches and canopies is realized; further, the cross-scale structure expression capability is enhanced through structure perception feature propagation; in the three-dimensional reconstruction stage, based on skeleton topology and parameter-driven modeling, in combination with complex area compensation and adaptive refinement strategy, high-fidelity reconstruction of forest structure is realized; finally, single-tree structure model and forest structure parameters are output. The present application improves the point cloud segmentation precision and reconstruction quality, and is suitable for forest resource investigation, carbon storage evaluation and ecological monitoring scenes.

The invention relates to the technical field of forest resource monitoring and management, and discloses a forest stand structure optimization and complementary planting method fusing ULS and BLS point clouds, which improves the acquisition precision of complex mountain forest structure information by fusing the ULS and BLS point clouds. The continuity and reliability of forest window identification are improved through the processes of threshold screening, edge detection, smoothing and the like; quantification of the forest stand structure quality is realized by constructing a comprehensive evaluation index composed of five types of space structure parameters; and complementary planting optimization is carried out by introducing a genetic algorithm, so that the globality and effectiveness of scheme search are enhanced. The method provides an integrated technical framework for forest window operation and space structure optimization of the mountain natural forest, and has good application potential.

The application discloses a forestry unmanned aerial vehicle point cloud and ground point cloud non-target automatic registration method and device. The method comprises the following steps: S1, extracting two kinds of data source breast height position point cloud slice data, respectively using DBSCAN clustering algorithm to extract single tree coordinate information; S2, determining the corresponding relationship of the two based on the starburst diagram composed of the single tree coordinates in the two kinds of data sources, and solving the initial transformation matrix; S3, using ICP algorithm to perform fine matching on the two kinds of data, and obtaining the final transformation matrix, S4, and then performing space coordinate system conversion on the ground laser scanning point cloud based on the unmanned aerial vehicle laser point cloud. In the case that complete forest structure information cannot be obtained from a single point cloud data source, the initial transformation matrix is obtained by extracting single tree coordinates and using the starburst diagram composed of the geometric relationship between the single trees, the transformation matrix is calculated by combining ICP fine registration, and finally the transformation from the ground point cloud coordinate system to the unmanned aerial vehicle point cloud coordinate system is realized.

The invention discloses a cypress low-efficiency mixed forest structure optimization transformation method, which comprises the following steps: collecting cypress and associated tree root system distribution depth, soil moisture content change value, canopy branch position angle and light spot coverage ratio in a forest stand to form a root system expansion attribute set; and constructing underground root system moisture redistribution identification data based on the communication relationship between the water absorption strength of different soil layers and the root system, and forming a moisture competition matrix between the cedarwood and the associated tree according to the underground root system moisture redistribution identification data. According to the method, through a multi-layer information coupling and dynamic feedback mechanism, a coordinated regulation and control system of overground light energy and underground carbon flow is established, and self-adaptive optimization and ecological stability improvement of the cypress and wood mixed forest structure are achieved.

The invention discloses a rapid restoration method for a degraded phyllostachys praecox forest, and belongs to the technical field of forestry ecological restoration. The method comprises the steps of degradation degree diagnosis and early-stage cleaning, old bamboo cleaning and bamboo forest structure optimization, soil deep ploughing and obstacle layer breaking, base fertilizer precise application, soil dressing improvement and later-stage maintenance and dynamic regulation and control. According to the improvement of the foreign soil, adaptive foreign soil is accurately selected, the laying thickness is determined according to the degeneration degree after pretreatment, laying is conducted in the bamboo rhizome vigorous growth period or the bamboo shoot bud differentiation period, and scientific watering is combined to promote fusion with original soil. Through a system integration technology, rapid soil function restoration, bamboo rhizome growth environment improvement and bamboo forest productivity improvement are achieved, and the method has the advantages of being rapid in restoration, good in effect, sustainable and wide in applicability and is suitable for restoration of phyllostachys praecox forests with different degradation degrees.

The present application relates to a space laser radar forest structure data correction method and system, and belongs to the technical field of space laser remote sensing and forest resource monitoring. In view of the four types of errors of GEDI data, such as geometric positioning deviation, slow variation distortion, terrain widening effect and crown structure influence, the present application adopts hierarchical layout strategy to layout UAV control points, establishes the same point relationship through waveform registration and calculates the weight; based on the regular grid control points, the overall geometric correction is carried out; based on the encryption control points, the local distortion correction is carried out; the physical compensation model is established to compensate the terrain widening effect; the statistical regression model is established based on the crown structure parameters and GEDI signal quality index to correct the crown height residual error; finally, the high-precision crown height convergence result is obtained through spatial smoothing processing, and the corrected data is output. The present application realizes the gradual accuracy improvement from the whole to the local and from the geometry to the height, has low cost, high precision and wide application range.

The invention provides a forest individual tree segmentation method and device based on trunk structure auxiliary labeling and multi-task global clustering, and the method comprises the steps: firstly obtaining a forest sample point cloud, carrying out the preprocessing of the forest sample point cloud, carrying out the preliminary segmentation of the preprocessed forest sample point cloud, and obtaining an individual tree segmentation result of each point; then, at least one round of regrowth operation based on the trunk center position and the trunk radius is executed on each single tree, and the final target point cloud of each single tree is obtained; and finally, performing semantic-level prediction and offset vector prediction by using the double-task prediction model, and generating individual tree instances based on semantic prediction and offset prediction results. The point-level semantic category and the offset vector pointing to the trunk center line are predicted at the same time under a unified framework, and high-precision and end-to-end automatic segmentation of the single tree in the complex forest point cloud is realized in combination with a global clustering strategy oriented to forest structure characteristics.

The invention relates to the technical field of debris flow disaster prevention and control, in particular to a debris flow pile forest prevention and control structure parameter optimization method based on DGPR-EI. According to the method, evaluation indexes of substance interception and energy dissipation are comprehensively considered, a sample set is generated in combination with a numerical simulation means, a response function is fitted by adopting dynamic Gaussian process regression (DGPR), an optimal parameter combination is searched by utilizing an expectation improvement (EI) algorithm, and convergence is ensured through a dynamic iteration strategy. The method solves the technical defects that in the prior art, quantitative indexes are lacked, the high-dimensional nonlinear problem of an objective function is difficult to deal with, and optimization is difficult under the condition of a small sample set. The method can significantly improve the scientificity and accuracy of the pile forest structure design, reduces the calculation cost, and is suitable for practical engineering application.

The application relates to the technical field of debris flow disaster prevention, in particular to a debris flow pile forest prevention and treatment structure parameter optimization method based on DGPR-EI. The application proposes an evaluation index which comprehensively considers material interception and energy dissipation, generates a sample set in combination with a numerical simulation method, adopts dynamic Gaussian process regression (DGPR) to fit a response function, searches for an optimal parameter combination by using an expectation improvement (EI) algorithm, and ensures convergence through a dynamic iteration strategy. The method solves the technical defects of the prior art, such as the lack of quantitative indexes, the difficulty in dealing with high-dimensional nonlinear problems of the objective function and the difficulty in optimization under the condition of a small sample set. The application can significantly improve the scientificity and accuracy of pile forest structure design, reduce the calculation cost, and is suitable for practical engineering application.

The invention provides an arid region mountain man-made forest construction layout optimization method and a related device, and relates to the technical field of forestry ecological engineering. Wherein the electronic equipment determines a to-be-afforestation area from a research area; obtaining a forest hydrological coupling model of the to-be-afforestation area; and simulating an optimal planting scheme of a man-made forest for the to-be-afforestation area through the forest hydrological coupling model. Thus, by obtaining the forest hydrological coupling model of the to-be-afforestation area, the hydrological model is linked with the multiple forest models, so that the hydrological process simulation result can dynamically drive forest growth simulation, and meanwhile, the forest structure parameters can be fed back to the hydrological process; and bidirectional coupling between moisture redistribution and vegetation response caused by factors such as terrain, slope direction and micro-terrain is realized. Therefore, after the to-be-afforestation area is determined, the model is used for simulating hydrological and growth responses under different planting schemes, and the adaptability of each scheme in a real environment can be scientifically evaluated.