210 results about "Forest resource" patented technology

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

The invention discloses a 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 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.

The invention provides a forest degeneration process identification and degeneration degree division method based on a time sequence, and relates to the field of forest resource degeneration restoration. The method comprises the following steps: acquiring surface reflectance data, and calculating an NBR index after preprocessing; fitting an NBR time sequence through a LandTrendr algorithm, and extracting sample place time sequence data based on random sampling and visual interpretation; generating an adversarial network and expanding sample point fitting data into a simulation data set in combination with a self-supervised learning technology; training CNN, random forest and BOSSVS time sequence classification models, and constructing a hybrid classification model through an integrated voting strategy; and identifying a forest degeneration process by using a hybrid classification model, and generating a degeneration degree diagram through reclassification. The method can achieve the efficient recognition of the complex degradation process of the forest region, has good time sequence adaptability and regional applicability, and provides technical support for the monitoring and management of ecological restoration of regional forests.

The invention discloses a forest resource dynamic authority management method and system based on multi-service collaboration, relates to the technical field of forestry service collaboration and information sharing, and aims to perform demand analysis on various service tasks in forest resource management, define related management roles and departments and clarify authority demands and responsibilities of the service tasks. According to the method, the dynamic permission allocation model is constructed, the permission granularity is refined to the subcompartment unit level, flexible allocation and dynamic adjustment of permissions are achieved, the flexibility and adaptability of permission management are greatly improved, permission allocation can be adjusted in real time according to the requirements of different service tasks and the change conditions of forest resources, and the permission allocation efficiency is improved. The tight matching of the authority and the actual business demand is ensured, the problem of business blocking or authority abuse caused by the fixed authority in the traditional authority management is avoided, and the efficiency and safety of forest resource management are effectively improved.

The invention discloses a multi-source collaborative checking system and method for the real quantity of natural resource assets, and relates to the technical field of natural resource management. The system comprises a data acquisition module used for acquiring multi-source data and real-time data and preprocessing the multi-source data and the real-time data; the knowledge graph processing module is used for constructing a forest resource knowledge graph according to the multi-source data and the real-time data; and the check analysis module is used for constructing an intelligent check model based on the knowledge graph constructed by the knowledge graph processing module. By automatically collecting and preprocessing multi-source data, constructing and dynamically maintaining a knowledge graph, and intelligently checking and analyzing and visually presenting a result based on the graph, efficient data integration, accurate and intelligent checking, trend prediction and active early warning are realized, and a scientific decision basis is provided for urban forest resource management.

The invention provides a bamboo forest disaster detection method and device based on an airborne radar, and relates to the technical field of remote sensing monitoring, and the method collects three-dimensional point cloud data of a to-be-detected bamboo forest through a laser radar, and the laser radar is carried on an unmanned aerial vehicle, thereby solving the problems of low efficiency and limitation of terrains in traditional manual treading. The data acquisition efficiency is improved; based on the three-dimensional point cloud data, determining the affected area and the normal area of the bamboo forest to be detected, thereby accurately and quickly positioning the affected area according to the three-dimensional point cloud data; determining disaster indexes of the to-be-detected bamboo forest based on the affected area and the normal area, so as to carry out detailed quantification on disaster conditions; and based on the disaster index, obtaining a disaster detection result of the to-be-detected bamboo forest. According to the bamboo forest disaster detection method based on the airborne radar, accurate and efficient detection of bamboo forest disasters is realized, and a decision basis can be provided for post-disaster recovery work, disaster-affected bamboo forest resource reutilization and the like.

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

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

The invention discloses a moso bamboo age identification method and system based on an improved YOLO11 model, and belongs to the crossing field of artificial intelligence and forestry resource monitoring. Aiming at the problems in the prior art that bamboo age identification depends on artificial experience, image processing is easily interfered by illumination, and a general model is insufficient in cross-scale texture feature capture and the like, the invention provides the following innovations: 1) an anti-reflection-texture decoupling fusion framework is designed, reflection high-frequency noise is eliminated through a GhostConv module, longitudinal textures are enhanced in combination with a CBAM channel-space attention mechanism, and the cross-scale texture features are not sufficiently captured; a C2PSA multi-scale pyramid is used for fusing the features under different illumination conditions; 2) constructing a multi-granularity age sensitive detection head, and extracting bamboo joint spacing features in a cross-scale manner by adopting a cavity convolution combination (1 * 1 / 3 * 3 / 5 * 5) of a Basic RFB module; and 3) introducing lightweight collaborative design, compressing the volume of the model to 12.5 M (42% less than that of YOLOv5s) by using depth separable convolution (kernel size = 2) of a C3k2 module and a GhostConv channel compression technology, and keeping edge sharpness at the same time. Through verification of 2086 annotated images in three places, the age identification accuracy of the method reaches 89.5% and is improved by 3.8% compared with a base line, 120ms real-time detection of a mobile terminal is supported, and the method can be efficiently used for bamboo forest resource investigation and dynamic monitoring.

The invention discloses a handheld forest region sample plot calibration surveying and mapping device and method, and relates to the technical field of forest resource investigation, and the device comprises a laser radar, a visual camera and a global navigation satellite system / inertial orientation positioning navigation system integrated navigation unit; when a user carries the device to move, the processing unit constructs a three-dimensional point cloud map and calculates a six-degree-of-freedom pose; when the signal of the global navigation satellite system is unavailable, calculating a current position by combining the three-dimensional point cloud map, the six-degree-of-freedom pose and inertial data; and the processing unit calculates parameters of the stumpage and the candidate area and generates a sample plot calibration surveying and mapping report. According to the invention, the device achieves the synchronous capturing of the characteristics of the trunk base and the canopy at a handheld height, an operator only needs to click and select an initial position on a touch screen, and the system combines laser instant positioning and map construction with a global navigation satellite system / inertial orientation positioning navigation system for tight coupling positioning. And automatically generating sample plot boundaries and ecological parameters conforming to regulations.

The invention discloses a forest point cloud branch and leaf separation method fusing double attention and edge perception, and relates to the field of forestry environment monitoring, the method is based on a forest point cloud branch and leaf separation network CLEANet, a classical encoder-decoder architecture is adopted, an encoder layer is composed of a down-sampling module and a channel-local point attention CLPA module, and the channel-local point attention CLPA module is composed of a down-sampling module and a channel-local point attention CLPA module. The decoder layer realizes feature recovery through combination of up-sampling, an edge perception module EAM and a multi-layer perceptron MLP, the CLPA module adaptively strengthens geometric detail and semantic feature expression through a double-attention mechanism and effectively captures wood and leaf component differences, the EAM module enhances perception of a network to a local geometric structure through a neighborhood feature propagation and fusion mechanism, and the local geometric structure is effectively captured. And characteristic mutation of the wood and the leaf at the boundary is captured. The method integrates a channel-local point attention mechanism and edge perception, has excellent robustness, good generalization ability and wide practical application potential, and provides powerful technical support for forest resource investigation and ecological environment monitoring.

The invention discloses a forest ecosystem carbon reserve determination method based on multi-source remote sensing data, and the method comprises the steps: constructing and analyzing a dominant tree species multi-temporal multi-feature classification data set according to Sentinel-1 and Sentinel-2 multi-source remote sensing data and research area forest resource checking vector data extraction; classifying the dominant tree species according to the multi-feature multi-temporal remote sensing data; and estimating the forest carbon reserves of different dominant tree species according to the optimal classification data set and the forest stock. According to the method, forest multi-feature and multi-temporal feature data are obtained by means of multi-source remote sensing data, and combined dimension reduction and separability analysis are performed, so that advantageous tree species spatial distribution is extracted, a forest stock and biomass linear model is fitted, and carbon reserve spatial distribution and features of a research area are estimated. And basic data and a practical basis are provided for further strengthening forest ecological system protection and implementing forest carbon reserve accurate improvement measures.

The invention relates to an intelligent forestry comprehensive monitoring management system based on the Internet of Things. The method comprises the following steps: transmitting an obtained thermal infrared forest region image of a to-be-analyzed forest region to an Internet of Things background server through a communication module; in the Internet of Things background server, processing the thermal infrared forest region image of the forest region to be analyzed by using a brightness component compensation module to obtain a compensated thermal infrared forest region image; in the background server of the Internet of Things, forest region infrared distribution global fluctuation feature extraction is carried out on the compensated thermal infrared forest region image to obtain forest region infrared distribution correlation fluctuation semantic feature vectors; and in the Internet of Things background server, a monitoring result is determined based on the forest infrared distribution correlation fluctuation semantic feature vector. Therefore, accurate identification of forest fire can be realized, timely early warning information is provided, powerful technical support and decision basis are provided for forestry management work, and sustainable development of forest resources and ecological environment is protected.

The invention discloses a high-sensitivity ratio-type fluorescent probe for detecting hypochlorous acid as well as a preparation method and application of the high-sensitivity ratio-type fluorescent probe. The N '-(4'-(6, 6-dimethyl-4, 5, 6, 7-tetrahydro-1H-5, 7-bridge methylene indazole-3-yl)-[1, 1 '-biphenyl]-4-methylene) malononitrile prepared by using a natural renewable forest resource beta-pinene derivative nopinone as a raw material can selectively react with hypochlorous acid, and the fluorescent color of the N'-(4 '-(6, 6-dimethyl-4, 5, 6, 7-tetrahydro-1H-5, 7-bridge methylene indazole-3-yl)-[1, 1'-biphenyl]-4-methylene) malononitrile is changed from yellow to cyan, so that the effect of the N '-(4'-(6, 6-dimethyl-4, 5, 6, 7 the maximum fluorescence emission wavelength is changed from 460 nm to 375 nm, and the compound can be used as a specific ratio type fluorescent probe for detecting hypochlorous acid and has a good application prospect.

The invention discloses an automatic forest checking method integrating point cloud precise segmentation and parameter inversion, and relates to the field of forest resource checking, and the method comprises the steps: constructing an automatic 3D forest checking frame with a double-branch point cloud segmentation network as a core, carrying out the preprocessing of forest point cloud data, extracting multi-scale features, and carrying out the segmentation of a point cloud segmentation network; performing mask scoring processing on the multi-scale features, and finally outputting a mask score, a semantic segmentation result and an instance segmentation result; dividing the point cloud into three types of semantic tags of ground, branches and leaves according to the semantic segmentation result, dividing the instance segmentation result into independent individual tree instances, performing convex hull or voxelization processing on the accurately segmented point cloud, inverting forest structure parameters, and completing automatic checking of forest resources. The automatic 3D forest checking framework is excellent in performance, efficiently and accurately draws the boundary of a single tree in a point cloud, realizes high-precision identification and separation of branches and leaves on a sample plot and a tree level, and promotes digitization, automation and intelligentization of forestry resource management.

The invention discloses a forest resource estimation method and device based on deep learning. Relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining initial point cloud data of a target forest region, and carrying out the preprocessing of the initial point cloud data, and obtaining target point cloud data; performing feature extraction on the target point cloud data through a target backbone network of a target deep learning model to obtain target feature information; processing the target feature information through a plurality of full connection layers of the target deep learning model to obtain an output result corresponding to each full connection layer; and obtaining the forest resource quantity of the target forest region according to the output result corresponding to each full connection layer. According to the method and the device, the problem that the efficiency of estimating the forest resources is relatively low due to the fact that the forest resources in the forest area are estimated through a sample plot checking method in the related technology is solved.

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.