187 results about "Sample plot" patented technology

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 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 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 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 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 carbon source and sink accounting method based on flux observation, an ecological system carbon reserve accounting system and a flux observation carbon source and sink accounting system are established, and the ecological system carbon reserve accounting system obtains carbon reserves of plants, soil, water and ground mud based on a sample point investigation method to estimate the carbon reserves of the ecological system. Estimating regional carbon reserves in combination with remote sensing survey; the flux observation carbon source sink accounting system is based on the constructed wetland carbon sink monitoring system and monitors data in real time; regional ecological system carbon reserve dynamic accounting is carried out by combining sample plot investigation with a carbon sink model and a remote sensing image technology, compared with the prior art, long-term fixed observation sample plots are set from multiple angles of lake wetland water, soil and gas generation, and Hongze lake wetland carbon sink observation data acquisition is carried out; and carrying out parameter adjustment and correction by utilizing a modern technical means and a large model technology, carrying out Hongze lake carbon sink model construction, and realizing Hongze lake carbon sink value accounting.

The invention discloses a vegetation biomass monitoring method fusing unmanned aerial vehicle hyperspectrum and Sentinel-2, and belongs to the field of ecological remote sensing information processing. In order to break through the data fusion bottleneck, a sample plot sample is collected to measure biomass, and a shrub different-speed growth equation is constructed; preprocessing the hyperspectral image of the unmanned aerial vehicle; segmenting and identifying grassland and shrubs; extracting feature inversion single-year biomass; constructing a buffer area to obtain sample plot AGB reference data; training the model by using a Sentinel-2 image; and long-time-sequence monitoring is realized through batch inversion. The method breaks through the bottleneck of data fusion, improves the inversion precision and generalization ability, reduces the cost, and supports long-time-sequence dynamic monitoring and multi-scene application.

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 grassland ecological evaluation, in particular to a dynamic reference index calculation method and system for grassland ecological quality evaluation. The method comprises the following steps: acquiring historical reference data of a non-degraded sample plot covered with different interference degrees in a target area, and constructing a reference system time sequence prediction model based on the historical reference data; based on pre-acquired historical monitoring data of a to-be-evaluated grassland, utilizing the reference system time sequence prediction model to obtain a dynamic reference index potential value; extracting an interference factor from the historical reference data, and setting an interference weight for the interference factor; and correcting the potential value of the dynamic reference index by using the interference weight based on the historical monitoring data and the historical reference data to obtain the dynamic reference index. The problem that in the prior art, a fixed reference index is difficult to dynamically respond to the change of the environment, so that the evaluation of the ecological condition of the grassland is inaccurate is solved, and the scientificity of reference index calculation is greatly improved.

The invention provides an urban green land carbon storage and sink quantification method based on remote sensing and field investigation, and relates to the technical field of environmental science and engineering, remote sensing technology and ecological assessment, and the method comprises the steps: obtaining a high-definition remote sensing image, combining a green land planning file, determining the distribution of various types of green lands, calculating an NDVI, and extracting an effective green land space; and generating a sampling point scheme according to the area proportion. Growing parameters of arbors and shrubs in the quadrat are investigated on site, biomass is calculated and converted into carbon reserves, an NDVI and carbon density model is established, and a carbon density spatial distribution diagram is generated. And constructing a carbon sink model based on the relationship between the age of the arbor and the carbon reserves, calculating the annual carbon sink, establishing a relationship model between the NDVI and the carbon sink, and generating a carbon sink spatial distribution diagram. And integrating the two diagrams to output carbon storage and sink capability evaluation results of different green land types, thereby providing a scientific basis for urban ecological planning.

A tree-dwelling primate identification method based on thermal infrared and visible light image fusion relates to the technical field of target detection, and comprises the following steps: synchronously acquiring a visible light image and a thermal infrared image in a target sample plot; performing image processing on the visible light image and the thermal infrared image; extracting thermal infrared features in the thermal infrared image and biological features in the visible light image; based on the extracted thermal infrared features and biological features, performing a three-layer matching mechanism of rough matching, static matching and biological constraint; carrying out image fusion on the visible light image and the thermal infrared image; carrying out model training and optimization; and outputting a species identification result. According to the method, adaptive infrared radiation correction enhancement is carried out on the complex inhabiting environment of the tree-dwelling primates in the high-altitude area, then thermal infrared features and visible light features are fused to carry out cross-modal feature three-layer matching and modeling, interference of environmental conditions can be effectively eliminated, the recognition precision is improved, and the recognition efficiency is improved. And a key technical support is provided for physiological monitoring of the tree-dwelling primates.

The invention provides a forest sample plot monitoring system, the monitoring system comprises a three-dimensional live-action modeling subsystem, an Internet of Things monitoring perception subsystem and a data management analysis subsystem, the three-dimensional live-action modeling subsystem is used for collecting sky point cloud data and ground point cloud data, and constructing a forest sample plot three-dimensional model; the Internet of Things monitoring and sensing subsystem is used for collecting tree growth associated information collected by a sensor and gathering and transmitting the tree growth associated information to the data management and analysis subsystem; and the data management and analysis subsystem is used for combining the forest sample plot three-dimensional model with the tree growth associated information, and analyzing to obtain forest accumulation variation and tree growth data. Through high-precision, high-efficiency and automatic reconstruction of the three-dimensional real scene of the forest sample plot, automatic, real-time and continuous monitoring of key growth parameters such as diameters at breast height of all sample trees in the sample plot is realized, and the intelligent level and operation efficiency of forest monitoring are improved.

The application provides a sample plot setting method and device, comprising the following steps: determining a base point in a to-be-investigated land; determining a plurality of positioning points with a preset distance from the base point based on a preset angle; and setting a unit sample plot in the to-be-investigated land according to all the positioning points. The sample plot setting method and device provided by the application have the advantages that the setting of the unit sample plot is completed through the determination of the base point and the positioning points, the practicability is high, the efficiency of sample plot setting is improved, the method is simple and easy to implement, and the required tools are few, so that the manpower and resources are effectively saved.

The invention provides a three-dimensional habitat visibility measurement method fusing ground-based and air-based laser radars, and the method comprises the steps: cutting ground-based laser radar point cloud data based on the sample plot size of a target forest region and the physiological morphological characteristics of a target animal; constructing a three-dimensional voxel model based on an occupancy grid algorithm and the clipped ground-based laser radar point cloud data, tracking a simulated animal sight line in the three-dimensional voxel model, calculating three-dimensional animal habitat visibility, calculating forest three-dimensional structure parameters based on the clipped air-based laser radar point cloud data, and calculating forest three-dimensional structure parameters based on the clipped ground-based laser radar point cloud data. A random forest model is constructed and trained based on the three-dimensional animal habitat visibility and the forest three-dimensional structure parameters, effective forest three-dimensional structure parameters are screened out, the precision of the random forest model is evaluated by adopting evaluation indexes obtained through taimen-taimen cross validation, and an optimal random forest model is obtained. And inputting the effective forest three-dimensional structure parameters into the optimal random forest model to predict the three-dimensional animal habitat visibility.

A method, a device, a storage medium and an equipment for observing hydrogen reservoir components in soil are provided by the present disclosure, including: obtaining the hydrogen components of soil pore water, the hydrogen components of organic matter, and the hydrogen components of lattice water corresponding to each sample; and obtaining the total hydrogen reservoir components of the target sample plot based on the hydrogen components of soil pore water, the hydrogen components of organic matter, and the hydrogen components of lattice water corresponding to the samples. The target sample plot is provided with a preset number of sampling soil profile points, each of the sampling soil profile points corresponds to n samples, and the sampling depth interval between any two adjacent samples in the same sampling soil profile point is consistent, so that the hydrogen reservoir components in the target sample plot can be accurately obtained.

The utility model belongs to the field of field experiment systems, and discloses a rain reduction experiment device for a high and cold peat land, which comprises a rain interception assembly, a support assembly and a water cut-off assembly, the rain intercepting assembly is used for guiding rainwater to the outside of the sample plot; the supporting assembly is used for supporting the rain intercepting assembly above the ground of the high and cold peat land; the water cut-off assembly comprises a waterproof piece which is used for surrounding the outer side of the supporting assembly and extends into the position below the ground of the high and cold peat land. The device disclosed by the utility model can well realize simulated rain reduction of the high and cold peat land, accurately obtain an experimental structure and avoid interference to the natural state of an ecological system.

The invention discloses a method for extracting parameters of a (subtropical) eucalyptus man-made forest based on an unmanned aerial vehicle laser radar. The method comprises the following steps: investigating a sample plot, and measuring and calculating actual forest parameters such as average diameter, average height, sectional area, stand volume and the like; acquiring and preprocessing high-density unmanned aerial vehicle laser radar point cloud data, and extracting three types of feature variables of height, density and vertical structure; deducing a model structural formula on the basis of a stock different-speed growth equation, and screening variables according to rules; constructing a power model by using a regular exhaustion method; the optimal model is screened out through model parameter estimation, inspection, significance analysis and the like. According to the method, the variable synergistic effect is mined through the exhaustion method, the multi-dimensional test system is constructed, and the exclusive model is customized, so that the problems of one-sided variable screening, insufficient model reliability and poor specific forest stand adaptability in the prior art are solved, high-precision estimation of core forest parameters is realized, and the method is suitable for large-scale popularization and application. And scientific and effective technical support is provided for refined management and sustainable operation of eucalyptus man-made forest resources.

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 discloses a prediction method of tree species structure of arbor forest in subtropical areas, and comprises the following steps: 1) dividing the tree species of arbor forest into three categories of pines, firs and broad-leaved trees, and establishing a prediction model according to volume proportion data of each tree species category obtained from fixed sample plots in previous forest resource surveys; 2) taking two-period survey data as modeling data, and fitting parameters by using STATA software; 3) substituting measured data of each sample plot into the prediction model for iterative calculation, obtaining volume proportion values of the future arbor forest in the two-period interval as prediction values; calculating average prediction values of the prediction values of the tree species proportions of all sample plots; and 4) comparing the prediction value of the volume proportion of the broad-leaved trees or the sum of the prediction values of the volume proportions of the pines and firs in the average prediction values of the tree species proportions with a critical value of coniferous and broad-leaved mixed forest, and determining whether artificial intervention needs to be applied in actual forestry management. The application can predict the tree species structure of arbor forest in future years, and has the function of guiding forestry production and operation activities.

To grasp a state of an environment where a plurality of trees such as forests exist while reducing time and cost required for work.SOLUTION: In the flight condition determination system 110 that determines a flight condition for performing forest measurement according to an embodiment, the processing device 111 acquires map information indicating a measurement target area 50 in which the unmanned aerial vehicle 1 is caused to fly and a plot area 50a that is a part of the measurement target area 50. The processing device 111 determines a flight condition of the unmanned aircraft 1 such that the total number of laser pulses 22 emitted per unit area is different between the plot area 50a and an off-plot area 50a that is an area other than the plot area 50b in the measurement target area 50.SELECTED DRAWING: Figure 13

The application discloses a canopy coverage considering aggregation index inversion method and correction method, relates to the technical field of remote sensing image processing and aggregation index inversion, collects remote sensing images of a vegetation canopy, extracts hot spot reflectivity and dark spot reflectivity respectively, calculates a normalized hot spot and dark spot index NDHD, calculates an initial value of an aggregation index CI according to a linear relationship between the NDHD and the CI, and corrects the initial value of the CI by using a canopy coverage Ccan, wherein an error between the initial value of the CI and a real value of the CI has a linear relationship with the Ccan. The application first establishes a CI correction model based on the Ccan, and solves the deviation problem of a traditional algorithm in a sample field with high or low Ccan. By improving a mainstream CI inversion algorithm based on the NDHD, the application significantly improves the precision of CI inversion, and provides more reliable technical support for forest ecological monitoring, carbon cycle research and remote sensing application.

This invention proposes a spatial mapping method and system for new bamboo shoot yield based on satellite-UAV fusion. Addressing the problem of remote sensing observation gaps caused by the underground growth of bamboo shoots, this invention constructs a multi-scale fusion framework of "satellite identification of potential high-yield areas + UAV inversion of old bamboo density." First, a new bamboo yield prediction model is constructed using old bamboo density and topographic factors as variables through ground-based sample plot surveys. Second, indices such as NDVI and LSWI are calculated using time-series imagery, and a decision tree model is used to identify potential new bamboo germination areas in high-yield bamboo forests. Then, based on high-resolution UAV imagery, a region growing algorithm is used to extract individual old bamboo plants and invert the spatial distribution of old bamboo density. Finally, using the potential areas as masks, old bamboo density and topographic factors are substituted to generate a spatial distribution map of new bamboo yield. This invention enables large-area, low-cost, and high-precision mapping of new bamboo shoot yield, providing core technical support for precise bamboo forest management.

The invention belongs to the technical field of carbon sink monitoring, and particularly discloses a scattered greenbelt carbon sink measuring and calculating device and method based on a box-type respiration method.The method comprises the steps that in the sample plot investigation stage, a gridding point distribution method is adopted to divide areas, and a quadrat covering different vegetation types and interference factors is randomly selected; and a dynamic acquisition and detection stage: setting time frequency parameters of dynamic acquisition, acquiring gas in an area according to the set time frequency parameters, simultaneously monitoring basic environment quality, vegetation coverage and man-made interference intensity in real time, simultaneously monitoring the running state of a sensor in real time, and if data transmission is abnormal, starting buzzing alarm by a data terminal so as to give an alarm. Abnormal time and abnormal types are automatically recorded; and a data correction calculation and output stage: calling and storing a corresponding correction model based on a microenvironment factor data type, calculating a carbon flux and a carbon sink amount according to a box-type method, and outputting the data according to two modes of real-time display and storage export.

The invention discloses an intelligent coupling analysis and modeling method for a forest structure and absorbed photosynthetically active radiation, and belongs to the technical field of forest ecological remote sensing, structural analysis and computer simulation. Focuses on fusion of airborne and handheld laser radar information, forest structure indexes are accurately inverted, and individual tree level attributes, neighborhood space structure indexes, forest gap structures, forest stand density SD and gradient SL are covered; three-dimensional radiation transmission simulation is carried out, high-precision three-dimensional forest reconstruction and layered APAR simulation are realized by constructing a single-tree three-dimensional model library and a digital sample plot, and photosynthetic spatial heterogeneity parameters are extracted; spearman correlation analysis is carried out based on the structure and APAR parameters, and a dominant structure mode and a space type are identified in combination with PCA dimension reduction and KMeans clustering; and constructing a typing structure-APAR coupling ensemble learning model based on a clustering result, and revealing a regulation and control mechanism of individual tree attributes and a neighborhood structure on APAR.