292 results about "Sample plot" patented technology

The invention relates to the technical field of remote sensing space information, in particular to a large-scale forest green carbon inversion method and system based on multi-source remote sensing data, and the method comprises the steps: resolving remote sensing parameters, and obtaining the crown breadth and height of a single-plant crown of a sample plot; taking the crown breadth and the height of the single tree crown of the sample plot as independent variables, and establishing different-speed growth models of different tree species; performing inversion based on original echo signal waveform data of the satellite-borne laser radar to obtain forest structure parameters; according to the forest structure parameters, calculating laser spot scale biomass by using a different-speed growth model; and based on the laser spot scale biomass and the multi-source remote sensing image features, using a U-Net deep learning model to obtain large-scale biomass data with continuous space, and obtaining a large-scale forest above-ground biomass distribution map according to the large-scale biomass data. According to the method, high-precision inversion from single-plant crown parameters to large-scale forest above-ground biomass is realized, and the efficiency of forest biomass estimation is improved.

The invention discloses a forest grass wet carbon sink metering method and system, and relates to the field of carbon sink metering, and the method comprises the following steps: 1, collecting and processing multi-source data, 2, fusing the data, aligning the multi-source data, adopting a geographic registration and time synchronization technology, and unifying a spatial resolution and a timestamp, and 3, extracting features. 4, constructing a carbon sink dynamic model; and 5, carrying out carbon sink visualization and decision support. According to the forest and grass wet carbon sink metering method and system, multi-source data collaboration is achieved, optical, radar and laser radar data are fused, the defect that traditional carbon sink metering depends on ground sample plot survey or single remote sensing data is overcome, carbon sink contributions of forests, grass and wet lands are accurately distinguished, the overall spatial resolution is higher, cloud and mist interference is avoided, the single data source error is solved, and the measurement accuracy is improved. Through multi-source data fusion and dynamic model optimization, high-precision measurement and real-time monitoring of forest grass wet carbon sink are realized.

The invention relates to an airborne and SLAM point cloud automatic registration method for a forest sample plot, and belongs to the field of laser point cloud data processing. The method comprises the following steps: cooperatively acquiring point cloud data of a forest land area through an airborne laser radar and SLAM equipment, and performing feature extraction on SLAM point cloud by adopting a random forest model to obtain crown edge contour points; segmenting the airborne point cloud by using a multi-level watershed algorithm and extracting crown edge contour points; using an FPFH algorithm to match position features of computer-borne data crown edge contour points and SLAM data crown edge contour points; and a transformation matrix is calculated through an RANSAC method, and the source point cloud is transformed to the reference of the target point cloud, so that complete spatial information description of the forest land scene is realized. According to the method, the edge contour points of the tree point cloud can be automatically extracted, the airborne point cloud and the SLAM point cloud data are registered, and the forest point cloud vertical structure information is fused, so that the forest structure information is finely inverted.

The invention provides a forest ecosystem carbon sink future scene simulation method and system, and belongs to the technical field of administrative management purpose information. Based on a high-precision curved surface simulation HASM method, processing the climate data and the soil data respectively, and extracting pixel values of the position of the forest sample plot in the climate raster data and the soil raster data into forest sample plot data to obtain a forest sample plot environment information data set; designing a plurality of future scenes of the forest ecosystem under different management measures; and simulating the carbon density of each future scene by using a Forest-DNDC model, predicting the carbon density of other areas except a forest sample plot by using a random forest model, and carrying out HASM simulation by taking a prediction result as an initial field and taking a simulation result output by the Forest-DNDC model as a control condition to obtain forest and soil carbon density raster data in the future scenes. According to the method, the simulation precision can be effectively improved, and a scientific basis is provided for formulating county scale forest management and climate alleviation policies in the future.

The invention discloses a fine individual tree segmentation method fusing air-based laser radar point clouds and ground-based laser radar point clouds, and aims to solve the challenges that the data volume of combined point clouds is large, crown staggering is complex in a dense forest environment, and part of tree trunk point clouds are lost due to shielding, and the method combines an individual tree segmentation strategy from top to bottom and an individual tree segmentation strategy from bottom to top. A weighted height-density map is adopted to carry out individual tree positioning, a mark control watershed algorithm is combined to realize preliminary tree crown region extraction, then multi-label local graph segmentation optimization is introduced, segmentation boundaries are corrected, an under-segmentation unit is optimized by adopting a shortest path, and a complete segmentation framework is formed. On the basis of airborne and foundation point clouds of six actually measured sample plots, the method is verified after registration and combination. The advantages of airborne point cloud and foundation point cloud are combined, and the proposed individual tree segmentation method provides technical support for refined forest modeling and ecological monitoring.

The invention discloses a forest global single-tree-level crown index system establishment method based on an airborne laser radar, and belongs to the technical field of intelligent forestry remote sensing investigation and monitoring. According to the method, on the basis of the unmanned aerial vehicle laser radar point cloud of the target forest stand, a derivative model is constructed, and then full-forest stand individual tree segmentation is carried out. According to the whole forest single-tree point cloud; a three-dimensional space extreme value detection method, an elevation detection slice segmentation method of an RANSAC (Random Sample Consensus) algorithm, a boundary adaptive detection method of horizontal projection and direction range decomposition, a canopy projection mutual embedding and contact height screening judgment method, a projection convex hull method based on a QuickHull algorithm and the like are respectively adopted to automatically extract the height, the crown length, the height under branches, the crown diameter and the effective crown length of a single tree; according to 17 individual tree crown indexes such as crown projection area and crown volume, a whole forest individual tree crown index system database is constructed through spatial topological correlation mapping. According to the method, the limitation of traditional sample plot investigation is broken through, and visual monitoring extraction and accurate establishment of the forest global single-tree-level crown growth index system are realized.

The invention discloses a method for reseeding and repairing alpine degraded grassland, which comprises the following steps: step 1, cutting: cutting native vegetation of a sample plot with the stubble height of 3-5cm; 2, sowing is conducted, before the rainy season comes, drilling is conducted on target grass seeds in a manual ditching mode, the target grass seeds are 2-3 grass seeds selected from each type of gramineae grass, leguminous grass and miscellaneous grass and mixed according to a certain proportion, and earthing is conducted after sowing is completed; 3, after-sowing management is conducted, agricultural measures such as impurity removal, fertilization and watering do not need to be conducted after sowing is completed, and grazing is forbidden for two years. According to the method, different functional types of plants and combinations suitable for the alpine region are screened out, the method suitable for reseeding restoration of the alpine degenerated grassland is formulated, the reseeding cost is controlled, meanwhile, plant communities high in yield, high in stability and diverse in function are built, and the problem of restoration of the degenerated grassland in the alpine region is solved.

The invention provides a vegetation surface biomass inversion method and device, electronic equipment and a storage medium. According to the invention, forest vegetation surface biomass field investigation is carried out by using data scanned by a knapsack laser radar and data measured manually, and actual measurement information of vegetation surface biomass in a sample plot, high-resolution panoramic image data in a sample plot range and high-precision knapsack three-dimensional laser radar point cloud data are obtained; scanning the sample plot by using an airborne laser radar, and extracting point cloud features capable of reflecting vegetation growth conditions from scanning data; then, the point cloud features serve as independent variables, total biomass obtained by processing data scanned by the backpack laser radar and actual measurement information serves as dependent variables, a biomass regression model is constructed, and the forest vegetation surface biomass is estimated through the model.

The invention provides a binary wood volume equation forest measurement method based on a ground-based laser radar. The method comprises the following steps: acquiring complete point cloud information scanned by the ground-based laser radar; processing the point cloud data to obtain tree structure parameters; fitting a plurality of cutting degree equation models through tree structure parameters to obtain a single-tree optimal cutting degree equation model; and a single-wood cutting degree-binary wood volume equation is constructed based on the single-wood optimal cutting degree equation, and the single-wood volume is calculated. According to the method, the traditional under-forest data acquisition work can be simplified, the tree species cutting degree equation can be updated for the sample plot forest more timely and accurately, the permanent damage to the tree caused by ground survey can be reduced, effective technical support is provided for the survey of the stand volume of a man-made forest and the estimation of the forest stand outturn percentage, and the method is suitable for popularization and application. Compared with a traditional method for estimating the volume and the stock of the single wood through a wood stock table, the method is higher in precision and better in flexibility, can be better used in the production practice process, and greatly saves manpower, material resources and financial resources.

The invention discloses a park scale vegetation carbon sink calculation method based on a visible light vegetation index, and the method comprises the following steps: obtaining a visible light orthographic image of a target park, and calculating a blue-green ratio index GBRI of the pre-processed visible light orthographic image, extracting and converting raster data of a vegetation coverage area in an integral pixel of the visible light orthographic image, and inputting the raster data into a visible light vegetation index calculation model to obtain a visible light vegetation index in the range of the target park; collecting the specific information of the vegetation to obtain the carbon reserve of the vegetation in each sample plot and the carbon sequestration of the vegetation in each sample plot; establishing a park vegetation carbon reserve estimation model by using the carbon reserve of the vegetation in each sample plot and the corresponding different visible light indexes in each sample plot; establishing a park vegetation carbon sequestration amount estimation model by using the carbon reserves of the vegetation in each sample plot and the corresponding different visible light indexes in each sample plot; according to the method, efficient and accurate calculation of the park vegetation carbon sequestration is realized.

The invention belongs to but not limited to the technical field of animal husbandry statistics, and particularly relates to an alpine meadow yak number calculation method and system based on an unmanned aerial vehicle image, and the method comprises the steps: S1, employing an unmanned aerial vehicle to shoot an orthoimage of a sample plot, and carrying out the statistics of the yak number; s2, splicing the shot images by using Pix4D software, screening a proper sample plot, and respectively counting the quantity of wet cow dung and the quantity of dry cow dung in a visual interpretation mode; and S3, carrying out linear fitting on the obtained data by using Origin 2024b, so as to obtain three relation curves. According to the method for estimating the number of the yaks through statistics of the number of the cow dung in the unmanned aerial vehicle image, the workload of data collection is greatly reduced, and the limitation that the number of the yaks cannot be obtained when the yaks are not grazed in the grassland is avoided. According to the invention, the data acquisition frequency of experimenters is effectively reduced, and the research cost is reduced.

The invention discloses a pheromone binding protein derived peptide capable of effectively monitoring sex pheromones of fall webworms and a biosensor, and belongs to the field of biosensors. At present, methods such as sample plot survey and sex traps commonly used in fall webworm monitoring have the problems of poor timeliness, high labor cost, insufficient data continuity and the like. In order to solve the problem, the pheromone binding protein-derived peptide provided by the invention has an amino acid sequence as shown in SEQ ID NO: 2, the biosensor comprises a substrate and an interdigital electrode, a single-walled carbon nanotube is attached to the electrode, and the electrode is also connected with the pheromone binding protein-derived peptide. The sensor can specifically detect sex pheromones of fall webworms, has no response to 17 plant volatile matters, and can detect sex pheromones released by as low as five live female fall webworms under laboratory conditions. The method has application potential in the fields of early pest monitoring, prevention and control efficiency improvement, ecological safety guarantee and the like.

The invention discloses a secondary forest development stage division method based on forest stand state characteristics, and belongs to the technical field of forest development stage division. Comprising the following steps: selecting an initial index set, performing index forward and standardization processing on sample plot survey data, and forming a data set; classifying the data by adopting a system clustering analysis method, checking and analyzing a state index influencing a classification result, and selecting plt; the index of 0.05 constitutes a secondary forest development stage division index system; carrying out weight assignment, then correcting the weight by adopting an entropy evaluation method, and determining the comprehensive weight of the index by utilizing a Lagrange multiplier method; the sum of the dimension-removed value of the division index and the comprehensive weight value product is a development stage value; according to an equidistant method, the development stage of the secondary forest is divided into a forest gap stage, a renewal stage, a differentiation stage, a built-up stage and a stable stage. Depending on forest age is avoided, and the method is suitable for multi-tree mixed secondary forests; the indexes are easy to obtain, and the division result can accurately guide forest management.

The invention discloses an automatic dynamic measurement method and device for long-term forest eco-hydrology water volume in the technical field of eco-hydrology water volume measurement, and the method comprises the specific steps: 1, selecting a measurement point, building a sample plot according to a standard, and selecting a plurality of experiment points meeting conditions in the sample plot; 2, installing a water quantity monitoring device at the experiment point meeting the condition; 3, measuring the trunk runoff, the penetration precipitation, the water content of a surface fallen leaf layer and the soil water content of the forest in a preset time period by using a water quantity monitoring device, and recording a drainage time point; and 4, calculating the flow velocity, the total flow and the forest water conservation amount according to the measured data. The device is convenient and practical, penetrating rainfall is intercepted and measured through the plastic film, stem flow is intercepted and detected through the runoff ring, the fixing claws are continuously pushed to enter a tree trunk through the telescopic connecting rods by means of the gravity of water in the water measuring cylinder to fix the whole device, and the data quality stability of long-term hydrological water volume measurement and the safety of the device are improved.

The invention relates to the technical field of forestry measurement, in particular to a forest sample plot investigation and measurement method and system based on fusion of multiple point cloud technologies. The forest sample plot investigation and measurement system comprises an ALS point cloud collector, an MLS point cloud collector, a TLS point cloud collector, a path planning module, a preprocessing module, a registration module, a classification extraction module, a database and a visualization module. According to the forest sample plot investigation and measurement method and system based on fusion of multiple point cloud technologies, all-around point cloud data acquisition is carried out in the air, the ground and marked fixed points of a forest through fusion of ALS point cloud acquisition, MLS point cloud acquisition and TLS point cloud acquisition technologies, different technical advantages are fully played, and the forest sample plot investigation and measurement method and system based on fusion of multiple point cloud technologies have the advantages of being high in accuracy and high in accuracy. The forest sample plot survey and measurement precision and efficiency are improved, multiple point cloud acquisition modes are fused and dynamically adjusted, the fusion strategy is automatically adjusted according to the data acquisition characteristics of the acquirer in different scenes, and the acquisition efficiency is improved.

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

The invention provides a method for evaluating grassland degradation based on microbial community structures and composition, and belongs to the technical field of grassland degradation degree evaluation. The method comprises the following steps: selecting a representative sample plot in a to-be-evaluated grassland degradation area, selecting a sample square in the sample plot, and collecting rhizosphere soil in the sample square; mixing rhizosphere soil of all quadrat belonging to the sample plot to obtain a soil sample; analyzing the OTU number of bacteria and the OTU number of fungi in the soil sample by adopting high-throughput sequencing; calculating a grassland degradation grade evaluation value according to the OTU number of the bacteria and the OTU number of the fungi; according to the grassland degradation grade evaluation value and the grassland degradation grade evaluation standard, grassland degradation evaluation is carried out, and an evaluation result is obtained. The grassland degradation grade evaluation value of the to-be-evaluated area is calculated by taking the microflora structure and composition of the to-be-evaluated area as evaluation indexes, and the grassland degradation grade of the to-be-evaluated area is determined in combination with the grassland degradation grade evaluation standard.

The invention discloses a vegetation carbon density and carbon reserve estimation method and device based on machine learning, and relates to the field of vegetation carbon density and carbon reserve accounting, the vegetation carbon density and carbon reserve estimation method based on machine learning mainly comprises the following steps: obtaining land cover and vegetation type data of a research area; setting a standard sample plot in the research area and investigating to obtain overground, earth surface and underground vegetation carbon density and carbon reserve data of the sample plot; performing preprocessing and information extraction on the land cover data of the research area to obtain remote sensing ecological factors of the sample plot; according to the sample plot remote sensing ecological factors and sample plot carbon density measured data, vegetation carbon density model construction and optimization are carried out, and an optimal estimation model is obtained; and according to the vegetation type and the vegetation area, utilizing the optimal estimation model to obtain the vegetation total carbon reserve in the research area. According to the vegetation carbon density and carbon reserve estimation method and device based on machine learning provided by the invention, rapid, dynamic and accurate estimation of multi-element vegetation carbon density and carbon reserve of regional scale forest grass wet and rough vegetation can be realized.

The invention discloses a forest above-ground biomass estimation method based on a remote sensing image. The method comprises the following steps: acquiring Sentinel-2 data and DEM data of a target area through a satellite; the Sentinel-2 data and the DEM data of the target area are input into the trained forest AGB estimation model, so that the forest AGB of the target area is estimated, and training of the forest AGB estimation model comprises the steps of setting a plurality of sample plots in the target area; obtaining Sentinel-2 data and DEM data of each sample plot, and extracting a reflectivity index, a vegetation index, biophysical characteristics and a terrain index; determining AGB of each sample plot; and training the forest AGB estimation model by using the training feature corresponding to each sample plot and the AGB of the sample plot. According to the method, the accuracy of forest AGB estimation is 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 relates to the technical field of plant ecological carbon sink measurement and calculation, in particular to a tea garden carbon reserve dynamic monitoring and prediction method based on meteorological data, a typical sample plot in a tea garden is selected, meteorological data and basic carbon reserve of the typical sample plot are acquired, and the meteorological data comprise real-time data and forecast data; establishing dynamic monitoring, and performing heat damage identification and freeze damage identification on real-time data; establishing a carbon reserve dynamic correction model; a carbon reserve prediction model is established, and when heat damage and / or freezing damage occur in prediction data, the carbon reserve dynamic correction model is used for prediction to obtain a prediction result; according to the method, the damage of high-temperature stress to the tea trees has a gradual superposition characteristic, high-temperature inhibits photosynthesis, so that calculation is performed in a summation form, cold waves increase respiratory consumption, and the damage mainly depends on the intensity peak value and the cooling rate of a single event; by providing more accurate dynamic measurement and calculation as the supplement of static measurement and calculation, the change condition of the carbon reserves of the tea garden can be reflected in time.

The invention discloses a forest vegetation health index construction and diagnosis method based on multi-source remote sensing data, and the method achieves the fusion of a feature level and a decision level of data through selecting sample plots containing different health levels, collecting the multi-source remote sensing data of a satellite, an unmanned plane and a ground sensor, and constructing a multi-source data fusion network. Indexes reflecting forest health are extracted from the fused data, a hierarchical clustering method is adopted for screening and standardization processing, a health index model is constructed, and a machine learning algorithm is utilized for optimizing the weight. And finally, calculating a forest health index based on the optimized model, dividing health levels, and generating a forest health level distribution map. The method is suitable for large-area forest vegetation health diagnosis, has the characteristics of high efficiency, accuracy and comprehensiveness, can remarkably improve the scientificity and rationality of forest health monitoring, and can be expanded and applied to the fields of fire / insect pest early warning, carbon sink evaluation and the like.

The invention discloses a forest carbon sink dynamic closed-loop regulation and control method and system based on multi-source monitoring data, and relates to the technical field of forest ecological monitoring and carbon sink management, and the system comprises a data collection layer which is used for obtaining multi-source monitoring data including carbon flux data, plant physiological data, microclimate data, soil flux data and sample plot survey data; the intelligent fusion layer is used for processing and feature extraction of multi-source monitoring data and outputting a standardized carbon sink driving factor; the dynamic prediction layer predicts the carbon sink trend through an integrated model by using the standardized carbon sink driving factor; and the regulation and control decision-making layer is respectively connected with the dynamic prediction layer and the forest ecosystem, comprises a regulation and control rule base and a feedback optimization unit, and is used for generating a quantitative regulation and control strategy and optimizing a model and a rule. Therefore, by adopting the forest carbon sink dynamic closed-loop regulation and control method and system based on the multi-source monitoring data, the precision and efficiency of forest carbon sink management can be improved, and real-time and accurate closed-loop regulation and control are realized.

The invention discloses a ground monitoring method for water and soil loss and carbon loss of a road slope, and belongs to the field of ecological monitoring. The method comprises the following steps: setting an engineering disturbance area and a natural contrast area according to regional water and soil loss, vegetation coverage and altitude gradient, and arranging an erosion pile sample plot to form a contrast observation network; removing impurities on the surface layer of the sample plot, collecting a soil sample, obtaining volume-weight data, and recording environmental parameters such as geographic position, altitude and gradient; the method comprises the following steps: pretreating a soil sample, measuring organic carbon content and volume weight, and obtaining carbon components and physical parameters; the exposed height of the erosion pile is measured regularly, erosion / accumulation modulus is calculated, and a space-time database is established in combination with weather, disturbance events and the like; and based on the erosion / accumulation data and the physical and chemical parameters, constructing a model to quantify carbon loss and carbon sequestration caused by water and soil loss. According to the method, through contrast point distribution, high-frequency observation and multi-parameter analysis, whole-process dynamic monitoring of complex terrain carbon migration is achieved, and the method is suitable for accurate evaluation of water and soil loss induced carbon loss in the field of ecological 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 fixed sample plot measuring device and method based on a high-precision foundation laser radar in the technical field of forest fixed sample plot measurement, and the device comprises a mobile vehicle body which is provided with a mounting seat, the mounting seat is provided with a processing device and a laser radar, and the two sides of the mobile vehicle body are each provided with a buffer assembly; the buffering assembly comprises a sleeve piece, a moving piece arranged in the sleeve piece in a sliding mode, an elastic piece arranged between the sleeve piece and the moving piece, a supporting plate arranged on the moving piece, a triggering assembly arranged in the sleeve piece and an air supply assembly communicating with the sleeve piece. The three-dimensional structure parameters of the vegetation are obtained through the laser radar, a traditional large fixed sample plot measurement method is improved, the monitoring precision of the individual height of the tree is greatly improved, and the problem that the height of the tree is difficult to measure in construction and monitoring in a traditional fixed sample plot or a large number of visual estimation modes are adopted is solved. And the precision and efficiency of forest fixed sample plot monitoring are improved.

The invention is suitable for the technical field of forest assessment methods, and provides an urban forest community carbon reserve assessment method and system based on multi-source data, and the method comprises the steps: obtaining research region information; the method comprises the following steps of: obtaining a geometrical relationship between an orientation in a panoramic image and a point cloud center direction after equipment installation, generating individual tree data units according to a relationship model, and binding the individual tree data units with unique numbers; extracting contour features of image areas corresponding to the individual tree data units, identifying whether the contour features have contradictions, performing secondary stripping on abnormal contours to obtain final individual tree data units, and performing species classification according to the final individual tree data units; a structure weighting factor is constructed by combining the area of the sample plot, a corresponding grade is judged according to the total carbon storage value of the whole sample plot, a theoretical carbon storage upper limit is calculated, the relative deviation rate of the current carbon storage is calculated according to the theoretical carbon storage upper limit and the total carbon storage value of the sample plot, and the method has the advantages that structure expression is refined, model matching is flexible, and the distinguishable grade of an evaluation result can be improved.

The present invention is applicable to the technical field of plot monitoring, and provides an ecological plot monitoring method and system based on computer vision, including the following steps: collecting aerial images of an ecological plot, where each aerial image corresponds to an aerial parameter information; performing edge cropping on the aerial images according to the aerial parameter information, and stitching all the edge-cropped aerial images to obtain an ecological plot image; performing image processing and image segmentation on the ecological plot image, segmenting the ecological plot image into several regions, and extracting key feature information of each region; identifying the ecological information of each region according to the key feature information, and at the same time obtaining the recognition credibility; determining the area range and collection coordinates of the regions that need to be recollected based on the recognition credibility; and re-determining the ecological information of each region according to the recollected aerial images. The present invention avoids blind re-sampling according to the recognition credibility, and further improves the accuracy and integrity of the ecological information.

The invention discloses a forest stock calculation method based on three-dimensional point cloud data and a neural network, and the method comprises the steps: carrying out the preprocessing of point cloud data, including point cloud denoising, filtering and normalization, and removing the redundant data, such as the surrounding ground feature environment and non-ground points, except for single tree information; a mark control watershed algorithm based on morphological filtering is adopted to mark a crown center point, individual tree segmentation is achieved, and individual tree parameters such as individual tree diameter at breast height, tree height and crown breadth are extracted; a neural network volume inversion model is established, the model takes the individual tree parameters as an input layer and the forest volume as an output layer, and an optimization algorithm is adopted to optimize the training process; and estimating the forest stock by using the stock inversion model, inputting the diameter at breast height, the tree height and the crown breadth in the sample plot checking data into the model, and outputting a forest stock estimation result.

The invention discloses a forest biomass and carbon library metering technology based on sample plot survey information, the technical scheme comprises a data acquisition system, a data processing and conversion module and a data website module, the data acquisition system is a basic part, and the data processing and conversion module is a core part. And the data website module provides data products and visual display for the user. Each tree is managed through a two-dimensional code, automatic quality control is performed based on historical survey data, forest vegetation biomass and carbon reserve situations of a quadrat and a sample plot are calculated by associating information of each tree of the quadrat and the sample plot, and a forest vegetation carbon sequestration rate and a carbon sequestration function are obtained by comparing previous survey data. According to the system, the investigation efficiency can be greatly improved, the forest vegetation carbon source function can be evaluated in real time, historical data can be used for automatic quality control of each re-investigation, investigation data can be uploaded in real time, and the working efficiency can be improved by more than 20 times through the application of the system.