Forest growth dynamic analysis system based on diameter at breast height monitoring ring

Abstract

The invention relates to the technical field of growth monitoring, in particular to a forest growth dynamic analysis system based on a diameter at breast height monitoring ring, which comprises a terrain adaptation module, a growth difference module, a deployment adjustment module, a factor weighting module and a trend dynamic analysis module. According to the method, by combining the geometrical relationship between the elevation change and the mounting axial angle, the orientation correction of the monitoring points can be realized according to the slope morphology, and the deployment density and the vertical height between the monitoring points can be adjusted according to the growth trend of the diameter at breast height, so that the monitoring network better fits the forest stand growth pattern, and an environmental factor dynamic weight structure is introduced; conjoint analysis is carried out on xylem and phloem expansion and contraction differences in illumination intensity fluctuation and soil humidity states, the modeling expression ability of local growth driving factors is improved, and a DBH dynamic change data structure with time continuity and space positioning ability is constructed. And the characterization accuracy and prediction capability of the evolutionary process of the forest diameter at breast height are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of growth monitoring, and in particular to a forest growth dynamics analysis system based on a diameter-at-breast-height (DBH) monitoring ring. Background Art

[0002] The field of growth monitoring technology mainly focuses on the assessment of the growth status and analysis of changing trends of biological individuals or groups over time. This field covers the dynamic collection and analysis of the growth behavior of plants or organisms based on remote sensing images, optical measurements, lidar, image recognition, time series modeling, etc., and combines model deduction technology to achieve quantitative expression and stage-by-stage estimation of growth factors. Common methods include time series regression modeling based on biophysical parameters, biomass estimation models, growth curve parameter inversion, multi-source data fusion modeling, etc., which are widely used in agriculture, forestry, ecosystem simulation and other fields. This field has high requirements for data continuity, model adaptability, parameter interpretability, etc., and emphasizes the ability to describe dynamics at a fine-grained level and the ability to model causal relationships between it and environmental variables.

[0003] Among them, the forest growth dynamics analysis system is a system used to analyze the growth process of trees or forest stands at a specific time scale. Its purpose is to build mathematical models and computational frameworks for predicting changes in forest stand structure, biomass accumulation, and carbon sequestration capacity assessment based on historical growth data and current physiological status, thereby supporting goals such as forestry resource management, ecosystem service assessment, and precise carbon sequestration monitoring. The system usually includes a growth variable extraction module, a time series modeling module, a parameter optimization module, and a prediction and deduction module. It supports growth state fitting and trend estimation based on the time dimension, and has structured output capabilities and forest stand spatial pattern analysis functions.

[0004] When performing dynamic analysis of forest growth, traditional analysis systems set monitoring points based on a regular deployment strategy, without adapting and correcting the orientation deviation of monitoring equipment in complex terrain environments, resulting in monitoring errors caused by differences in slope direction. At the same time, in the modeling of breast diameter growth, the differential distribution brought about by spatial growth competition was not dynamically reflected, resulting in insufficient capture of local growth intensity variations. The role of environmental factors was treated with fixed weights, and the light shading and water status of different points in time-series monitoring were not differentially expressed, limiting the adaptability of the modeling results in heterogeneous forest stand structures. For example, in areas with significant terrain undulations or dense canopy overlap, the modeling output is easily distorted, affecting the accurate description of carbon sink estimation and forest stand structure evolution trends. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a forest growth dynamic analysis system based on a diameter at breast height monitoring ring.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a forest growth dynamics analysis system based on a diameter-at-breast-height monitoring ring, the system comprising: The terrain adaptation module obtains the digital elevation model grid cells within the deployment area of ​​the DBH monitoring ring, extracts the angle between the normal direction of the regional slope and the original installation axis of the DBH monitoring ring, determines whether the angle difference is within the set allowable range, records the correction angle value of each monitoring point, and generates a terrain adaptation angle set for the DBH monitoring ring; The growth difference module calculates the variation range of the annual growth of the diameter at breast height between each group of adjacent monitoring points based on the terrain adaptation angle set of the diameter at breast height monitoring ring, and classifies them according to the distribution of the variation range to obtain a growth competition difference level set; The deployment adjustment module extracts the ratio of the vertical height from the maximum radial expansion point of the trunk base to the ground surface to the maximum horizontal projection diameter of the canopy based on the growth competition difference level set, sets the elevation range for points exceeding the range according to the ratio increment, and adds it to the three-dimensional deployment coordinate information to generate the monitoring ring spatial deployment information; The factor weighting module calls the spatial deployment information of the monitoring ring, sets the weight ratio in the model factor according to the light change trend, records the weight structure information corresponding to light and humidity at each monitoring point, and generates a weighted distribution table of modeling factors.

[0007] As a further solution of the present invention, the terrain adaptation angle set of the breast diameter monitoring ring is specifically the slope change direction identification, the installation axial adjustment angle and the deployment point correction number; the growth competition difference level set includes the breast diameter growth fluctuation amplitude classification, the adjacent monitoring point competition level label and the spatial difference distribution level; the monitoring ring spatial deployment information includes the compressed deployment spacing, height adjustment value and three-dimensional coordinate encoding; the modeling factor weighted distribution table is specifically the illumination factor weight value, the humidity factor weight value and the weight adjustment interval label.

[0008] As a further solution of the present invention, the terrain adaptation module includes: The slope calculation submodule obtains the digital elevation model grid cells within the DBH monitoring ring deployment area, extracts the elevation value of each grid cell and the horizontal spacing between adjacent cells, calculates the inverse tangent value based on the elevation difference and the horizontal spacing, determines whether the inverse tangent value exceeds the slope threshold, screens the slope areas that meet the conditions, and generates an identification value for the slope exceeding the limit area; The direction angle submodule calls the slope exceeding limit area identification value, extracts the corresponding slope normal direction parameter and the original installation axial parameter of the breast diameter monitoring ring, calculates the direction angle value between the two parameters, determines whether the direction angle value exceeds the set angle reference range, establishes a quantitative relationship of regional direction deviation, and generates an angle difference determination coefficient; The angle correction submodule calls the recorded direction deviation value based on the angle difference determination coefficient, obtains the actual azimuth offset fed back by the gyroscope in the deployment area, compares the difference between the offset and the angle determination value, and determines whether it is lower than the angle tolerance. If so, the correction angle parameters of the corresponding monitoring point are recorded, the installation direction correction information of the breast height monitoring ring point is established, and the terrain adaptation angle set of the breast height monitoring ring is generated.

[0009] As a further embodiment of the present invention, the growth difference module includes: The period extraction submodule extracts the diameter at breast height change sequence of multiple consecutive periods of the monitoring point based on the terrain adaptation angle set of the diameter at breast height monitoring ring and the recorded monitoring point identification information, performs structured merging according to the period duration, and generates a diameter at breast height period sequence set; The fluctuation calculation submodule obtains the annual growth of the diameter at breast height of adjacent monitoring points within the same period based on the diameter at breast height period sequence set, performs difference processing between the growth amounts, extracts the change amplitude and makes a difference judgment on the fluctuation threshold, calculates and obtains the growth difference response value of the monitoring point pair, screens the difference between the response value and the amplitude reference interval, obtains the fluctuation exceeding limit point group, and establishes the diameter at breast height fluctuation response information; The grade classification submodule calls the DBH fluctuation response information, sets the grade distribution interval value and determines the interval grade of the point in turn, assigns the corresponding grade mark, constructs a label identification set that distinguishes the regional growth status differences, and establishes a growth competition difference grade set.

[0010] As a further solution of the present invention, the formula for calculating the growth difference response value of the monitoring point pair is specifically: ; in, Indicates monitoring point With monitoring points The growth difference response value between Indicates monitoring point In the cycle The normalized value of the annual growth rate of DBH, Indicates monitoring point In the cycle The normalized value of the annual growth rate of DBH, Indicates monitoring point The normalized value of the horizontal distance between adjacent monitoring points in space, Indicates monitoring point The normalized value of the horizontal distance between adjacent monitoring points in space, Indicates monitoring point The altitude value, Indicates monitoring point The altitude value, Indicates monitoring point The estimated tree height of the corresponding tree, Indicates monitoring point The estimated tree height of the corresponding tree.

[0011] As a further solution of the present invention, the deployment adjustment module includes: The spacing compression submodule obtains the original deployment spacing of each type of point and the change in diameter at breast height growth between adjacent points based on the growth competition difference level set, sets the compression ratio according to the standard spacing compression rule, and performs spacing compression based on the average growth rate of the corresponding category. It calculates and obtains the compressed deployment spacing value of each point, binds the compressed result to the corresponding monitoring point number, and establishes spacing compression adjustment information; The ratio extraction submodule calls the spacing compression adjustment information, collects the vertical height from the maximum radial expansion point of the trunk base to the ground at each point, and collects the maximum horizontal projection diameter of the canopy, calculates the ratio between the two and records whether it exceeds the set ratio interval boundary, and generates a monitoring point height ratio group; The coordinate generation submodule is based on the monitoring point height ratio group. According to the points that exceed the set ratio range, it obtains the ratio increment and matches the corresponding standard value of the elevation amplitude. It inputs the deployment spacing in the spacing compression adjustment value into the three-dimensional coordinate generation sequence together, outputs the three-dimensional coordinate data of each point, and establishes the monitoring ring space deployment information.

[0012] As a further solution of the present invention, the formula for calculating the compressed deployment spacing value of each point is specifically: ; in, Indicates the The compressed deployment spacing value of each monitoring point, Indicates the The original deployment spacing of the points, Indicates the The average annual growth rate of DBH between a point and its adjacent points, Indicates the The normalized value of the maximum horizontal projection diameter of the canopy corresponding to each point, Indicates the The normalized value of the vertical height from the maximum radial expansion point at the base of the trunk to the ground, Indicates the The standard spacing compression adjustment reference value corresponding to each point.

[0013] As a further solution of the present invention, the factor weighting module includes: The light trigger identification submodule calls the three-dimensional coordinate value of each monitoring point based on the spatial deployment information of the monitoring ring, collects the monitoring data of the canopy light sensor within a continuous time period according to the coordinate position, and determines whether it is in a continuous low light state according to the set light intensity threshold. If it meets the threshold, the point weight adjustment trigger flag is set to obtain the light trigger state label set; The shading weight construction submodule collects the canopy shading rate values ​​of the corresponding points based on the light trigger state label set, extracts the change trend of the light monitoring sequence in chronological order, calculates the relative proportion of the shading rate in the model factor according to the set light trend benchmark, obtains the proportional distribution value of the light factor at each point, and obtains the light factor weight distribution value set; The humidity weight adjustment submodule calls the light factor weight distribution value set, collects the volume moisture content value recorded by the soil moisture sensor corresponding to the point in a continuous period, and determines whether it exceeds the moisture content threshold. If it exceeds, the difference between the xylem expansion rate and the phloem contraction rate in the diameter at breast height monitoring data is extracted, and the light weight value is deducted or adjusted according to the set rules based on the moisture status to obtain the weighted distribution table of the modeling factors.

[0014] As a further embodiment of the present invention, the system further comprises: The trend dynamic analysis module is based on the weighted distribution table of modeling factors, and according to the weight structure of each monitoring point, a weighted combination is performed under a unified time window according to the corresponding weight coefficient. A curve group reflecting the dynamic change trend of the diameter at breast height at each point is constructed, and each group of curves is bound to a spatial position to construct a diameter at breast height change data structure with time continuity and spatial positioning capabilities, thereby obtaining the results of forest diameter at breast height evolution analysis; The forest DBH evolution analysis results include spatial location node index, weighted time series group and DBH response trajectory structure.

[0015] As a further solution of the present invention, the trend dynamic analysis module includes: The weighted combination submodule extracts the DBH change sequence, canopy light monitoring sequence, and soil moisture monitoring sequence recorded at the point during the entire monitoring period based on the weighted distribution table of the modeling factors, and performs weighted combination processing within a unified time window according to the corresponding weight coefficients to generate a weighted data combination sequence; The time curve generation submodule constructs a data sequence reflecting the dynamic change trend of the diameter at breast height according to the weighted data combination sequence in chronological order, judges the stage continuity characteristics in combination with the change rate trend, identifies the boundaries of the time periods in the sequence and splices them into a time-continuous curve expression to obtain the dynamic trend curve group of the diameter at breast height; The spatial binding construction submodule is based on the dynamic trend curve group of the diameter at breast height, calls the three-dimensional coordinate data of the point in the spatial deployment information of the monitoring ring, binds the dynamic trend curve group of the diameter at breast height with the three-dimensional spatial coordinates according to the point correspondence, and combines them into a data set with a dual structure of time series and spatial index to establish the forest diameter at breast height evolution analysis results.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by combining the geometric relationship between elevation changes and installation axial angles, the azimuth correction of monitoring points can be achieved according to the slope morphology, and the differential distribution of radial growth can be used to dynamically identify the growth competition relationship in space. The deployment density and vertical height between monitoring points are adjusted according to the growth trend of diameter at breast height, so that the monitoring network is more in line with the forest stand growth pattern. The dynamic weight structure of environmental factors is introduced, and the differences in xylem and phloem expansion and contraction under light intensity fluctuations and soil moisture conditions are jointly analyzed to improve the modeling expression ability of local growth driving factors, and construct a data structure of dynamic changes in diameter at breast height with time continuity and spatial positioning capabilities, thereby enhancing the characterization accuracy and prediction ability of the forest diameter at breast height evolution process. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 is a system flow chart of the present invention; Figure 2 Schematic diagram of the system framework of the present invention; Figure 3 This is a flow chart of the terrain adaptation module of the present invention; Figure 4 is a flow chart of the growth difference module of the present invention; Figure 5 A flowchart for deploying the adjustment module for the present invention; Figure 6 This is a flow chart of the factor weighting module of the present invention; Figure 7 This is a flow chart of the trend dynamic analysis module of the present invention. DETAILED DESCRIPTION

[0019] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0020] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0021] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same.

[0022] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0023] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0024] See also Figure 1 , a forest growth dynamic analysis system based on DBH monitoring rings, the system includes: The terrain adaptation module obtains the digital elevation model grid cells within the deployment area of ​​the DBH monitoring ring, calculates the inverse tangent of the elevation change and horizontal spacing between adjacent cells as the slope parameter, screens areas where the slope exceeds the set threshold, extracts the angle between the normal direction of the regional slope and the original installation axis of the DBH monitoring ring, and combines the actual azimuth offset obtained by the gyroscope to determine whether the angle difference is within the set allowable range. The corrected angle value of each monitoring point is recorded to generate a terrain adaptation angle set for the DBH monitoring ring. The growth difference module extracts the DBH monitoring data of multiple consecutive periods from the monitoring points based on the terrain adaptation angle set of the DBH monitoring ring. It calculates the variation range of the annual DBH growth between each group of adjacent monitoring points, determines whether the variation range exceeds the set standard fluctuation range, and marks the points that exceed the range. It then divides them into different levels according to the distribution of the variation range, constructs a label classification set that expresses the differences in spatial growth status, and obtains a growth competition difference level set. The deployment adjustment module obtains the original deployment spacing and the change in DBH growth between adjacent points based on the growth competition difference level set, performs hierarchical compression deployment for each level according to the set spacing adjustment rules, records the compressed spacing value, extracts the ratio of the vertical height from the maximum radial expansion point of the trunk base to the ground surface to the maximum horizontal projection diameter of the canopy, determines whether the ratio exceeds the set ratio range, sets the increase amplitude for points that exceed the range according to the ratio increment, and adds it to the three-dimensional deployment coordinate information to generate the monitoring ring spatial deployment information; The factor weighting module calls the spatial deployment information of the monitoring ring, collects the continuous monitoring values ​​of the canopy light sensor within a certain time period according to the spatial position of each point, and determines whether the weight adjustment trigger condition is met based on whether the light level is continuously in a low-intensity state. If so, the canopy shading rate value of the current point is extracted, and the weight ratio in the model factor is set according to the light change trend. The volume moisture content value of the soil moisture sensor in a continuous period is collected to determine whether it is in a high moisture state. If the conditions are met, the difference between the xylem expansion rate and the phloem contraction rate recorded by the breast height monitoring ring is extracted, and the ratio of the light factor is adjusted according to the set rules. The weight structure information corresponding to light and humidity at each monitoring point is recorded to generate a weighted distribution table of modeling factors. The trend dynamic analysis module is based on the weighted distribution table of modeling factors. According to the weight structure of each monitoring point, it extracts the breast diameter change sequence, canopy light monitoring sequence, and soil moisture monitoring sequence recorded at the point during the entire monitoring period, and performs weighted combination under a unified time window according to the corresponding weight coefficient. It constructs a curve group reflecting the dynamic change trend of the breast diameter at each point, calls the three-dimensional spatial coordinates of the point in the spatial deployment information of the monitoring ring, binds each group of curves to the spatial position, and constructs a breast diameter change data structure with time continuity and spatial positioning capabilities to obtain the results of forest breast diameter evolution analysis.

[0025] The terrain adaptation angle set of the diameter at breast height monitoring ring specifically includes the slope change direction identification, installation axial adjustment angle and deployment point correction number. The growth competition difference level set includes the diameter at breast height growth fluctuation amplitude classification, adjacent monitoring point competition level label and spatial difference distribution level. The spatial deployment information of the monitoring ring includes the compressed deployment spacing, height adjustment value and three-dimensional coordinate encoding. The modeling factor weighted distribution table specifically includes the light factor weight value, humidity factor weight value and weight adjustment interval label. The forest diameter at breast height evolution analysis results include spatial position node index, weighted time series group and diameter at breast height response trajectory structure.

[0026] See also Figure 2 and Figure 3 ,The terrain adaptation module includes a slope calculation submodule, a direction angle submodule, and an angle correction submodule; The slope calculation submodule obtains the digital elevation model grid cells within the DBH monitoring ring deployment area, extracts the elevation value of each grid cell and the horizontal spacing between adjacent cells, calculates the inverse tangent value based on the elevation difference and the horizontal spacing, determines whether the inverse tangent value exceeds the slope threshold, screens the slope areas that meet the conditions, and generates an identification value for the slope exceeding the limit area; To obtain the digital elevation model grid cells within the deployment area of ​​the DBH monitoring ring, it is necessary to call the terrain surface data file obtained in the terrain survey. Each cell is a regular grid structure. Under the grid size of 1 meter side length, the elevation value of the center point of each cell is extracted one by one, and the elevation values ​​of the eight adjacent cells around it and the horizontal distance between the center points are extracted in pairs. The horizontal distance is based on the straight-line distance between the center points. or After determining the elevation difference between two points, the inverse tangent function is used to calculate the elevation difference and the horizontal distance. For example, the elevation difference between a center point and its adjacent unit center point is , the horizontal distance is , then the corresponding slope value is , compare the value with the set slope threshold, if the threshold is set to , it is judged as an over-limit unit. The setting of the slope threshold refers to the equipment stability error caused by the deviation of the breast height monitoring ring equipment from the ground normal when it is installed. If the slope exceeds The risk of equipment overturning increases when the equipment body support structure is designed to tolerate a maximum tilt angle of the ground. , so set As the slope threshold, it increases as the ground shear strength index decreases. Direction adjustment: In all grid cells, the above judgment is performed on each pair of adjacent cells and marked whether they exceed the limit. After the marking is completed, the adjacent exceeding-limit cell areas are clustered and merged to form a slope area boundary index set. The area number and the corresponding grid position coordinates are recorded in the index set, and then the slope exceeding-limit area identification value is generated; The direction angle submodule calls the slope exceeding limit area identification value, extracts the corresponding slope normal direction parameter and the original installation axial parameter of the breast diameter monitoring ring, calculates the direction angle value between the two parameters, determines whether the direction angle value exceeds the set angle reference range, establishes a quantitative relationship between the regional direction deviation, and generates the angle difference determination coefficient; Call the regional position in the slope exceeding limit area identification value, and extract the slope normal direction parameters pre-generated in the digital elevation model for each grid cell under the regional number. The normal direction is in the form of a unit vector, and the direction angle can be expressed as an azimuth. Extract the installation axial parameters from the original installation record file of the diameter at breast height monitoring ring in the cell. The installation axial direction is set for the monitoring ring to follow the main axis direction of the tree trunk, usually to The direction difference between the normal direction angle and the installation axial angle in the same unit is calculated using the angle formula Calculate the direction angle That is the angle between the two vectors. If the normal direction is , installation axial direction is , then the angle value is , judge whether the angle value exceeds the set angle reference range. The setting of the angle reference range is based on the response deviation of the breast diameter sensor to the radial expansion value under different installation error angles. After constructing the error mapping relationship, when the angle exceeds The recording error exceeds , so the base range is set to When the sensor accuracy increases with temperature drift, its error amplification rate is exponential, and the reference range needs to be dynamically adjusted down to to The interval is set as If the angle value exceeds the reference value, it is determined to be a deviation. The angle difference of each unit is recorded and its corresponding relationship with the area number is established. The deviation angle results in all areas are summarized to form the direction deviation statistics of each area, and finally the angle difference determination coefficient is generated; The angle correction submodule calls the recorded direction deviation value based on the angle difference determination coefficient, obtains the actual azimuth offset fed back by the gyroscope in the deployment area, compares the difference between the offset and the angle determination value, and determines whether it is lower than the angle tolerance. If so, the correction angle parameters of the corresponding monitoring point are recorded, the installation direction correction information of the breast height monitoring ring point is established, and the terrain adaptation angle set of the breast height monitoring ring is generated.

[0027] According to the direction deviation value recorded in the angle difference determination coefficient, the current azimuth offset of each monitoring point in the deployment area is obtained from the gyroscope sensor data set. The sensor data is stored in the form of recording once per minute. The time point data closest to the deployment moment is extracted as the actual offset value. The offset value vector is established with the monitoring point number as the index, and then the absolute difference is calculated with the angle difference under the same number. If the offset is , the angle difference is , the difference between the two is The difference is compared with the set angle tolerance. The setting of the angle tolerance refers to the installation allowable range of the diameter at breast height monitoring ring under different angle errors. The directional identification of the radial expansion measurement will not be affected. The relative error is not more than , thus setting the angle tolerance to , and control its fluctuation range according to the equipment calibration error distribution to If the deviation value is lower than the angle tolerance, it is determined that the current monitoring point does not need additional correction. Otherwise, the point is recorded as needing correction, and the weighted average of the actual offset value and the angle difference is used as the correction angle parameter. The weight setting is based on the stability index of the two measured values. If the gyroscope stability is , the angle difference stability is , then the correction angle parameter is ,in Indicates the The proportion of the item's measurement value in the weighted average, For the corresponding angle value, all satisfy , for all points that meet the correction conditions, a corresponding relationship table between the monitoring point number and its correction angle is established, and finally a set of terrain adaptation angles for the DBH monitoring ring is generated.

[0028] See also Figure 2 and Figure 4 ,The growth difference module includes a period extraction submodule, a fluctuation calculation submodule, and a level classification submodule; The period extraction submodule extracts the DBH change sequence of multiple consecutive periods of the monitoring point based on the terrain adaptation angle set of the DBH monitoring ring and the recorded identification information of the monitoring point. It then performs structured merging based on the period duration to generate a DBH period sequence set. Obtain the identification information of the monitoring point recorded in the terrain adaptation angle set of the DBH monitoring ring, and match the DBH change sequence recorded by each monitoring point in the original data set one by one. The sequence continuously records the DBH micro-change data in units of hours. First, extract multiple complete annual cycle segments by timestamp, distinguish them with one year as the cycle unit, and construct annual DBH subsequences for the DBH records of the same monitoring point in different annual cycles. Then, standardize them uniformly according to the length of the monitoring time, set the length of each annual cycle to 365 days, and perform linear interpolation and interpolation smoothing on the cycles with leap years or data missing dates. For example, the data segment with two consecutive days missing is filled with the weighted average of the DBH values ​​of the adjacent four days. After structuring in the time dimension, all annual DBH sequences of each monitoring point are merged into a multi-sequence structure divided by year to form a standardized DBH change cycle set. The sequence obtained in this step does not contain units and is used for subsequent fluctuation calculations after unified processing to finally generate a DBH cycle sequence set. The fluctuation calculation submodule obtains the annual growth of the diameter at breast height of adjacent monitoring points within the same period based on the diameter at breast height period sequence set, performs difference processing on the growth amounts, extracts the change amplitude and compares it with the difference judgment of the fluctuation threshold, calculates the growth difference response value of the monitoring point pair, screens the difference between the response value and the amplitude reference interval, obtains the fluctuation limit point group, and establishes the diameter at breast height fluctuation response information; The specific formula for calculating the growth difference response value of the monitoring point pair is: ; in, Indicates monitoring point With monitoring points The growth difference response value between Indicates monitoring point In the cycle The normalized value of the annual growth rate of DBH, Indicates monitoring point In the cycle The normalized value of the annual growth rate of DBH, Indicates monitoring point The normalized value of the horizontal distance between adjacent monitoring points in space, Indicates monitoring point The normalized value of the horizontal distance between adjacent monitoring points in space, Indicates monitoring point The altitude value, Indicates monitoring point The altitude value, Indicates monitoring point The estimated tree height of the corresponding tree, Indicates monitoring point The estimated height of the corresponding tree; The Growth Difference Response Value (GDR) is a composite indicator used to measure the annual growth differences in DBH between spatially adjacent monitoring points. This value not only accounts for absolute differences in DBH growth but also factors in spatial location (including horizontal distance and elevation) and tree height, quantifying the response to competition differences caused by local heterogeneity in the growing environment. This indicator is primarily used to construct a growth competition difference grading set. By assessing the response values ​​between adjacent points, we can identify areas of localized competition intensity within the forest, providing a spatial grading basis for strategic deployment.

[0029] The formula consists of two parts: Diameter at breast height growth difference item: ; This represents the normalized difference in growth amplitude within the same period, corrected by the adjacent horizontal distance. The closer the distance, the greater the difference, reflecting local interference.

[0030] Elevation / Tree Height Difference Item: ; Measuring the growth difference trend caused by elevation difference, the taller the tree, the stronger its buffering ability against elevation change.

[0031] According to the annual DBH variation trend of each monitoring point recorded in the DBH cycle series, the spatial adjacency relationship of the monitoring points is called to process any two spatially adjacent point pairs. First, the monitoring points and In the same cycle The difference calculation of the annual growth rate of DBH under 、 The original values ​​in millimeters need to be uniformly normalized. Normalization uses the maximum and minimum interval mapping method. Set the period The maximum value of DBH growth of all monitoring points is 5.0mm, and the minimum value is 1.0mm. The growth value is 3.5mm, then its normalized value is: ; If the monitoring point The growth value is 2.0mm, then its normalized value is: ; The horizontal spatial distances between the two points are then obtained as 4m and 6m respectively. Normalization is performed on them. Assuming that the maximum and minimum distances in the entire area are 10m and 1m, the normalized values ​​are: ; ; Further acquisition of monitoring points and The altitude values ​​of the two points are 720m and 745m respectively, and the estimated tree heights corresponding to the two points are 16m and 18m respectively; Substitute the above parameters into the following growth difference response value calculation formula: ; Substituting specific values, we can calculate: ; ; final ; This gives the monitoring point and In the cycle The growth difference response value under the above conditions is 27.178, indicating that there is a great deviation in the growth performance of DBH and the environmental difference. The threshold setting refers to the natural distribution of the growth trend and environmental difference of most point pairs in the forest area throughout the year. The fluctuation threshold is set by adding twice the standard deviation of the normalized response mean of the regional annual growth difference. If the statistical mean is 0.5 and the standard deviation is 0.3, the threshold is 1.1. The current point pair value is 1.133, which is obviously exceeded, so it is marked as a fluctuation abnormal point pair. By traversing all point pairs and summarizing the number of all points that exceed the set threshold, the concentrated distribution position of the point in the monitoring period is obtained to generate the DBH fluctuation response value.

[0032] The grading submodule calls the DBH fluctuation response information, sets the grade distribution interval value, determines the interval grade of the point in turn, assigns the corresponding grade mark, constructs a set of label identifiers that distinguish regional growth status differences, and establishes a growth competition difference grade set; Call the fluctuation limit point group selected from the DBH fluctuation response value, and calculate the corresponding value of each point. The value establishes a growth fluctuation level label, which is divided into multiple level intervals according to different value ranges. For example, the intervals are divided into: is level 1, is level 2, is level 3, For each point, the highest level among all participating point pairs is counted and recorded as the comprehensive fluctuation level of the point. Corresponding to the number of the point, a one-to-one correspondence set between the monitoring point number and the level mark is constructed. The setting of the level interval refers to the forest stand structure stability analysis. Level 1 represents a high consistency area, and level 4 represents an area with abnormal fluctuations or extreme growth differences. Finally, the set is output as a reference label set for evaluating the degree of spatial growth dynamic differences in the diameter at breast height monitoring area, and a growth competition difference level set is generated.

[0033] See also Figure 2 and Figure 5 ,The deployment adjustment module includes a spacing compression submodule, a ratio extraction submodule, and a coordinate generation submodule; The spacing compression submodule obtains the original deployment spacing of each type of point and the change in DBH growth between adjacent points based on the growth competition difference level set. It sets the compression ratio according to the standard spacing compression rule and performs spacing compression based on the average growth rate of the corresponding category. It calculates and obtains the compressed deployment spacing value of each point, binds the compressed result to the corresponding monitoring point number, and establishes spacing compression adjustment information. The formula for calculating the compressed deployment spacing value of each point is as follows: ; in, Indicates the The compressed deployment spacing value of each monitoring point, Indicates the The original deployment spacing of the points, Indicates the The average annual growth rate of DBH between a point and its adjacent points, Indicates the The normalized value of the maximum horizontal projection diameter of the canopy corresponding to each point, Indicates the The normalized value of the vertical height from the maximum radial expansion point at the base of the trunk to the ground, Indicates the Standard spacing compression adjustment reference value corresponding to each point; The compressed spacing value represents the actual spacing required to reduce the initial deployment plan for each monitoring point, after adjustments are made to local growth intensity and canopy structure during DBH monitoring ring deployment. This is used to generate spatial deployment information for monitoring rings, enhancing monitoring resolution in highly competitive areas by compressing deployment density, and rationally allocating resources to avoid monitoring redundancy.

[0034] Calculation logic: This formula combines three structural factors to dynamically adjust the deployment spacing: 1. DBH growth factor :The larger the value, the more intense the local growth. The more dense the planting spacing should be, and the higher the compression ratio. :The greater the imbalance between the canopy outreach and the vertical support of the trunk, the greater the difference in space occupation, and the higher the deployment density should be. : It plays a regulating role, limiting the compression range of the spacing so as not to exceed the operable range.

[0035] According to the point categories identified in the growth competition difference level set, the original deployment spacing of each type of point and the change in the diameter at breast height between adjacent points are obtained. The point set is divided by category number, and the original deployment spacing value of each point is extracted separately. , in meters, obtain the annual growth of the diameter at breast height recorded by its adjacent points in the same monitoring period, perform normalization processing on them, and obtain the mean growth rate of the diameter at breast height , where the normalization method uses maximum and minimum scaling to map all growth amounts in the monitoring area to the interval [0, 1]. For example, if the maximum value is 8.4 mm and the minimum value is 1.6 mm, the normalized result of the original value of 6.0 mm is , and then extract the maximum horizontal projection diameter of the canopy corresponding to the point Vertical height from the maximum radial expansion point at the base of the trunk to the ground , both data are normalized by the standard value in the same area, and the preset standard spacing compression adjustment reference value is called , the unit is set to 1 meter, and all the obtained parameters are substituted into the formula: ; The meaning of each parameter in the formula is as follows: :Indicates the The compressed deployment spacing of each monitoring point, in meters, is the core output value obtained by this submodule; :Indicates the The original deployment spacing of each point, in meters, is derived from the initial planned distance during deployment; :Indicates the The average annual growth rate of the diameter at breast height of a point and its spatially adjacent points in the current period is normalized to eliminate the interference of unit differences on the compression ratio calculation; :Indicates the The maximum horizontal projection diameter of the canopy at each point is normalized to a dimensionless value, reflecting the individual space occupancy; :Indicates the The vertical height from the maximum radial expansion point at the base of the tree trunk to the ground is also normalized for spatial structure comparison; :Indicates the The compression adjustment reference value at each point is the standard base of the adjustment unit, in meters.

[0036] Subscript : Used to identify the monitoring points as index variables.

[0037] The overall calculation structure of the formula is as follows: First, calculate the mean growth rate of DBH The square root of is used to measure the average growth potential of trees at a point; Then calculate and The absolute difference is divided by the compression adjustment reference value , to construct the structural deviation ratio of the canopy-base ratio; The two parts of the results are added together to form the denominator of the overall structural growth evaluation index; Subtract the inverse of the ratio from 1 to express the compression factor; Finally, the compression scale factor is multiplied by the original deployment spacing , output compression deployment spacing .

[0038] The benefit of the formula is that by introducing the mean value of breast height growth and the difference in canopy-base structure ratio The combined calculation of the two makes the deployment compression scale take into account both the individual growth capacity and the spatial morphology of the tree, avoiding adjusting the deployment spacing based on a single growth value, thereby improving the pertinence and rationality of point structure adjustment in actual deployment.

[0039] If the instance parameters are set as: rice, (after normalization), 、 (all are normalized values), rice; Substituting into the formula we get: ; The results show that the compressed spacing at point 5 is significantly smaller than the original spacing, reflecting the low DBH growth and large structural proportion deviation. Therefore, the deployment requires significant compression of the spatial layout to generate a spacing compression adjustment value. This result will serve as a basic parameter in the subsequent ratio extraction and three-dimensional deployment steps.

[0040] The ratio extraction submodule calls the spacing compression adjustment information, collects the vertical height from the maximum radial expansion point of the trunk base to the ground at each point, and collects the maximum horizontal projection diameter of the canopy, calculates the ratio between the two, and records whether it exceeds the set ratio interval boundary, generating a monitoring point height ratio group; Call the monitoring point information corresponding to the spacing compression adjustment value, and obtain the vertical height from the maximum radial expansion point of the trunk base to the ground point by point and the maximum horizontal projection diameter of the canopy , calculate the ratio , according to the preset ratio interval boundary setting, such as the normal ratio interval is [1.2, 2.5], judge Whether it falls within the range, if it exceeds it, it will be marked as an out-of-limit point, for example, a point rice, Meter, then , higher than the upper limit, record the ratio increment of the point , and then generate a monitoring point height ratio value group; Based on the monitoring point height ratio group, the coordinate generation submodule obtains the ratio increment for each point exceeding the set ratio interval and matches it with the corresponding elevation amplitude standard value. This is input into the three-dimensional coordinate generation sequence together with the deployment spacing in the spacing compression adjustment value. The three-dimensional coordinate data of each point is output to establish the monitoring ring spatial deployment information. According to the monitoring point height ratio value group that has been marked as exceeding the limit, obtain its ratio increment value , matching the preset standard value group of the elevation amplitude, assuming that each increase of 0.1 in the ratio corresponds to a 0.2-meter elevation, then in the above example, the ratio increment of 0.17 matches an elevation of 0.34 meters. Then extract the compressed deployment spacing recorded in the spacing compression adjustment value of the point. , the two values ​​are jointly input into the three-dimensional coordinate generation sequence, and the three-dimensional coordinate information is output based on the origin and relative position relationship of the terrain coordinate system. For example, if the horizontal coordinate origin is (100, 200), if this point is the 10th deployment point, the horizontal coordinate is (105, 204), and the vertical coordinate is z = 3.9 meters after adding the elevation value of 0.34. Then its final spatial coordinate is (105, 204, 3.9). All points are processed in this way to establish the monitoring ring spatial deployment information.

[0041] See also Figure 2 and Figure 6 ,The factor weighting module includes the illumination trigger recognition submodule, the shading weight construction submodule, and the humidity weight adjustment submodule; The light trigger recognition submodule calls the three-dimensional coordinate values ​​of each monitoring point based on the spatial deployment information of the monitoring ring. It collects the monitoring data of the canopy light sensor within a continuous time period according to the coordinate position, and determines whether it is in a continuous low light state based on the set light intensity threshold. If it meets the threshold, it sets the point weight to adjust the trigger flag and obtain the light trigger state label set; Call the three-dimensional coordinate value of each monitoring point in the monitoring ring space deployment information, obtain and parse its The three-dimensional spatial position is combined with the sunlight path of the deployment area, and the light intensity monitoring data recorded by the canopy light sensor at each point in the continuous cycle is selected. The light monitoring sequence of each point is sorted out according to the unified time window structure, and the light intensity threshold is set according to the set light intensity threshold. Determine whether it is in a continuous low-light state, where The minimum light intensity requirement in μmol / m2·s is set based on the minimum light threshold required for effective photosynthesis in the tree canopy. It is often set based on the local tree species' sunlight sensitivity and measured data for the latitude region. In this example, it is set to If the light intensity at the point is lower than the value for three consecutive monitoring cycles, the point is marked as triggering the low light state and recorded as "1", otherwise it is "0". A Boolean sequence representing the light trigger state is generated, and then the point set that meets the low light trigger condition is screened and identified, and the trigger mark is adjusted by assigning a weight, and finally a light trigger state label set is generated.

[0042] The shading weight construction submodule collects the canopy shading rate values ​​of the corresponding points based on the light trigger state label set, extracts the change trend of the light monitoring sequence in chronological order, calculates the relative proportion of the shading rate in the model factor according to the set light trend benchmark, obtains the proportional distribution value of the light factor at each point, and obtains the light factor weight distribution value set; Based on the light trigger state tag set, the canopy shading rate data of each marked point is collected. The data unit is percentage, extracted according to the time period sequence, and the light monitoring change trend curve is constructed to extract the change gradient of the shading rate in each monitoring period. , and calculate the proportion of the corresponding illumination item in the modeling factor system. The calculation method refers to the set illumination trend benchmark , which is the reference gradient of the average change of the shielding rate within a certain time period. In this example, it is set to / cycle, indicating that if the rate of change of the light trend exceeds this benchmark, it is considered a significant trend; the shading rate ratio is normalized and distributed according to the following proportional formula: ; in For the Each point corresponds to the light factor weight, is a minimum constant used to prevent division by zero errors and is set to 0.01. The illumination factor weight distribution of all trigger points is constructed through this formula, and finally the illumination factor weight distribution value set is obtained.

[0043] The humidity weight adjustment submodule calls the light factor weight distribution value set, collects the volume moisture content value recorded by the soil moisture sensor at the corresponding point in a continuous period, and determines whether it exceeds the moisture content threshold. If it exceeds, it extracts the difference between the xylem expansion rate and the phloem contraction rate in the diameter at breast height monitoring data, deducts or adjusts the light weight value according to the set rules based on the moisture status, and obtains the weighted distribution table of the modeling factors; Call the light factor weight distribution value set to further collect the volume moisture content value recorded by the soil moisture sensor at the corresponding point , in units of m3 / m3, performs a structured summary of moisture records within a continuous time period and determines whether it exceeds the moisture content threshold The threshold is set based on the soil water holding capacity and tree water demand of the typical forest in this monitoring area, and is set as If the moisture content of a point in any period of three cycles is higher than this value, it is marked as a high humidity state. Then the xylem expansion rate, which is closely related to water dynamics, is extracted from the DBH monitoring data. Phloem contraction rate , the unit is mm / cycle, perform difference operation , and then adjust the weight according to the set rules: ; in The empirical maximum difference is set to 0.8 mm / cycle. According to this rule, the original light factor weight of each high-humidity state point is modified. The deduction amplitude is proportional to the expansion and contraction difference, and finally a weighted distribution table of modeling factors is generated.

[0044] The formula operation logic is as follows: The light factor weight is constructed by the ratio of the shading rate change gradient to the trend benchmark to quantify the contribution of light change to the modeling. The second formula introduces the difference in the response of the diameter at breast height change under the influence of humidity as the basis for the adjustment amplitude of the light factor, which helps to simulate the growth response dynamics of plants under humidity fluctuation conditions.

[0045] The benefit of the formula is that by constructing the light weight distribution through the linkage of the two factors of the shading rate change trend and the breast diameter variability, the influence of a single factor can be limited and corrected, making the factor weighting more stable and closer to the real process of physiological drive, which helps to improve the robustness and interpretability of the modeling factor system.

[0046] When a certain example point is 、 、 、 When: , ; ; The results show that the light factor weight dropped significantly from the initial 2.396 to 0.599, indicating that the high volatility of water content inhibited the independent driving effect of light on the dynamic changes of DBH. Finally, the light weight value of each point in the weighted distribution table of the modeling factors was output.

[0047] See also Figure 2 and Figure 7 ,The trend dynamic analysis module includes a weight combination submodule, a time curve generation submodule, and a ,spatial binding construction submodule; The weighted combination submodule extracts the DBH change sequence, canopy light monitoring sequence, and soil moisture monitoring sequence recorded at the point during the entire monitoring period based on the weighted distribution table of modeling factors, and performs weighted combination processing within a unified time window according to the corresponding weight coefficients to generate a weighted data combination sequence; Call the normalized weight values ​​of the light factor, humidity factor, diameter at breast height factor, etc. recorded at each point in the modeling factor weighted distribution table, determine the parameter structure that needs to be included in the weighted calculation of the corresponding point in the modeling cycle, obtain the diameter at breast height change sequence, canopy light monitoring sequence, and soil moisture monitoring sequence recorded at the point during the entire monitoring period, slice and combine these three types of sequences according to a unified time window, and identify the normalized weight coefficients as 、 、 , corresponding to the diameter at breast height factor, light factor, humidity factor in the first The proportion of each point, setting the time window length to 7 days and sliding calculation, generates a unified weighted combination data sequence in a weighted summation manner, and its expression is: ; in For point In time The weighted combination value of 、 、 are the normalized values ​​of DBH, light and humidity factors at that moment, respectively. The weighted multiplication process is used to enhance the synergistic weight contribution of multiple factors at a single moment, and finally generate a weighted data combination sequence.

[0048] The time curve generation submodule constructs a data sequence reflecting the dynamic change trend of DBH according to the weighted data combination sequence in chronological order, judges the stage continuity characteristics based on the change rate trend, identifies the boundaries of the time periods in the sequence and splices them into a time-continuous curve expression to obtain the DBH dynamic trend curve group; Call the recorded values ​​of each time point in the weighted data combination sequence, reconstruct the sequence trend in chronological order, extract its change rate according to the gradient segment formed by the continuous growth or decline trend, and divide the sequence into sections based on the change in the sign of the change rate. For example, the combination value sequence of a certain point within 7 days is , the rate of change series is , it can be identified that the first 4 days are a continuous growth segment and the last 3 days are a continuous decline segment. The growth / decline boundary point is used as the segmentation node, and each segment is spliced ​​to form a dynamic trend time curve of the diameter at breast height. The curve point set is constructed according to the trend segment in each cycle, and the point sets are connected in sequence according to the time index order between the point sets to form trend curves expressing different change stages. Finally, a dynamic trend curve group of the diameter at breast height corresponding to the point position is constructed.

[0049] The spatial binding construction submodule uses the dynamic trend curve group of DBH to call the three-dimensional coordinate data of the points in the monitoring ring spatial deployment information. According to the point correspondence relationship, the dynamic trend curve group of DBH is bound to the three-dimensional spatial coordinates, and the combination is combined into a data set with a dual structure of time series and spatial index to establish the results of forest DBH evolution analysis. Call the monitoring ring space deployment information 3D coordinate data of monitoring points , the corresponding DBH dynamic trend curve group is bound to the spatial coordinates of the point as a time series object, the curve groups of all points are unified with the coordinate set, and a tree data structure is constructed with the spatial index as the primary key and the time trend as the subset. Multiple continuous curve segments are mounted under each spatial point, and their time index tags are recorded synchronously. , forming a The multi-dimensional data structure of the index is finally combined to form the forest DBH evolution analysis results, which are used for subsequent dynamic visualization and interpretation of evolution laws.

[0050] The symbols in the above formula are explained as follows: : midpoint of weighted combination sequence At the moment The comprehensive value of :Point The breast diameter factor weight is a normalized value with a value range of [0, 1]; :Point The illumination factor weight, normalized value; :Point Humidity factor weight, normalized value; 、 、 :For point In time The three monitoring values ​​above have been normalized. The symbol “·” represents multiplication, expressing the weighted contribution of each factor to the total value at the current moment. The addition “+” represents the linear superposition of multiple factor contributions, which is used to form the comprehensive value at the moment. The benefit of the formula is that by introducing three normalized factors, namely diameter at breast height, light and humidity, into the weighted combination structure, and adjusting the differentiated weights of each point, a growth state characterization mechanism tailored to local conditions is achieved. This can more accurately express the dynamic contribution relationship of growth driving factors under time-varying environmental conditions, which is conducive to the modeling results reflecting the real changing trends of time series dynamics.

[0051] For example, the three normalized values ​​of a certain point on a certain day are 、 、 , and its corresponding weight is 、 、 , then the weighted combination value is: ; The results show that the weighted comprehensive growth response of the point at this moment is 0.496. Combined with the trend changes, its continuous growth state is judged, and a complete time period trend is formed in the subsequent curve generation. Then, the point coordinates are bound in space to complete the modeling structure construction, supporting the spatiotemporal linkage expression of the forest diameter at breast height evolution analysis results.

[0052] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0053] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0054] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0055] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0056] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0057] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0058] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0059] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0060] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media that can store program code, such as USB flash drives, mobile hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0061] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A forest growth dynamics analysis system based on DBH monitoring rings, characterized by: The system comprises: The terrain adaptation module obtains the digital elevation model grid cells within the deployment area of ​​the DBH monitoring ring, extracts the angle between the normal direction of the regional slope and the original installation axis of the DBH monitoring ring, determines whether the angle difference is within the set allowable range, records the correction angle value of each monitoring point, and generates a terrain adaptation angle set for the DBH monitoring ring; The growth difference module calculates the variation range of the annual growth of the diameter at breast height between each group of adjacent monitoring points based on the terrain adaptation angle set of the diameter at breast height monitoring ring, and classifies them according to the distribution of the variation range to obtain a growth competition difference level set; The deployment adjustment module extracts the ratio of the vertical height from the maximum radial expansion point of the trunk base to the ground surface to the maximum horizontal projection diameter of the canopy based on the growth competition difference level set, sets the elevation range for points exceeding the range according to the ratio increment, and adds it to the three-dimensional deployment coordinate information to generate the monitoring ring spatial deployment information; The factor weighting module calls the spatial deployment information of the monitoring ring, sets the weight ratio in the model factor according to the light change trend, records the weight structure information corresponding to light and humidity at each monitoring point, and generates a weighted distribution table of modeling factors.

2. The forest growth dynamic analysis system based on the diameter at breast height monitoring ring according to claim 1 is characterized in that: The terrain adaptation angle set of the breast diameter monitoring ring specifically includes the slope change direction identification, the installation axial adjustment angle and the deployment point correction number; the growth competition difference level set includes the breast diameter growth fluctuation amplitude classification, the adjacent monitoring point competition level label and the spatial difference distribution level; the monitoring ring spatial deployment information includes the compressed deployment spacing, height adjustment value and three-dimensional coordinate encoding; the modeling factor weighted distribution table specifically includes the illumination factor weight value, the humidity factor weight value and the weight adjustment interval label.

3. The forest growth dynamic analysis system based on the diameter at breast height monitoring ring according to claim 2 is characterized in that: The terrain adaptation module includes: The slope calculation submodule obtains the digital elevation model grid cells within the DBH monitoring ring deployment area, extracts the elevation value of each grid cell and the horizontal spacing between adjacent cells, calculates the inverse tangent value based on the elevation difference and the horizontal spacing, determines whether the inverse tangent value exceeds the slope threshold, screens the slope areas that meet the conditions, and generates an identification value for the slope exceeding the limit area; The direction angle submodule calls the slope exceeding limit area identification value, extracts the corresponding slope normal direction parameter and the original installation axial parameter of the breast diameter monitoring ring, calculates the direction angle value between the two parameters, determines whether the direction angle value exceeds the set angle reference range, establishes a quantitative relationship of regional direction deviation, and generates an angle difference determination coefficient; The angle correction submodule calls the recorded direction deviation value based on the angle difference determination coefficient, obtains the actual azimuth offset fed back by the gyroscope in the deployment area, compares the difference between the offset and the angle determination value, and determines whether it is lower than the angle tolerance. If so, the correction angle parameters of the corresponding monitoring point are recorded, the installation direction correction information of the breast height monitoring ring point is established, and the terrain adaptation angle set of the breast height monitoring ring is generated.

4. The forest growth dynamic analysis system based on the diameter at breast height monitoring ring according to claim 3 is characterized in that: The growth difference module includes: The period extraction submodule extracts the diameter at breast height change sequence of multiple consecutive periods of the monitoring point based on the terrain adaptation angle set of the diameter at breast height monitoring ring and the recorded monitoring point identification information, performs structured merging according to the period duration, and generates a diameter at breast height period sequence set; The fluctuation calculation submodule obtains the annual growth of the diameter at breast height of adjacent monitoring points within the same period based on the diameter at breast height period sequence set, performs difference processing between the growth amounts, extracts the change amplitude and makes a difference judgment on the fluctuation threshold, calculates and obtains the growth difference response value of the monitoring point pair, screens the difference between the response value and the amplitude reference interval, obtains the fluctuation exceeding limit point group, and establishes the diameter at breast height fluctuation response information; The grade classification submodule calls the DBH fluctuation response information, sets the grade distribution interval value and determines the interval grade of the point in turn, assigns the corresponding grade mark, constructs a label identification set that distinguishes the regional growth status differences, and establishes a growth competition difference grade set.

5. The forest growth dynamic analysis system based on DBH monitoring ring according to claim 4 is characterized in that: The formula for calculating the growth difference response value of the monitoring point pair is specifically: ； in, Indicates monitoring point With monitoring points The growth difference response value between Indicates monitoring point In the cycle The normalized value of the annual growth rate of DBH, Indicates monitoring point In the cycle The normalized value of the annual growth rate of DBH, Indicates monitoring point The normalized value of the horizontal distance between adjacent monitoring points in space, Indicates monitoring point The normalized value of the horizontal distance between adjacent monitoring points in space, Indicates monitoring point The altitude value, Indicates monitoring point The altitude value, Indicates monitoring point The estimated tree height of the corresponding tree, Indicates monitoring point The estimated tree height of the corresponding tree.

6. The forest growth dynamic analysis system based on DBH monitoring ring according to claim 5 is characterized in that: The deployment adjustment module includes: The spacing compression submodule obtains the original deployment spacing of each type of point and the change in diameter at breast height growth between adjacent points based on the growth competition difference level set, sets the compression ratio according to the standard spacing compression rule, and performs spacing compression based on the average growth rate of the corresponding category. It calculates and obtains the compressed deployment spacing value of each point, binds the compressed result to the corresponding monitoring point number, and establishes spacing compression adjustment information; The ratio extraction submodule calls the spacing compression adjustment information, collects the vertical height from the maximum radial expansion point of the trunk base to the ground at each point, and collects the maximum horizontal projection diameter of the canopy, calculates the ratio between the two and records whether it exceeds the set ratio interval boundary, and generates a monitoring point height ratio group; The coordinate generation submodule is based on the monitoring point height ratio group. According to the points that exceed the set ratio range, it obtains the ratio increment and matches the corresponding standard value of the elevation amplitude. It inputs the deployment spacing in the spacing compression adjustment value into the three-dimensional coordinate generation sequence together, outputs the three-dimensional coordinate data of each point, and establishes the monitoring ring space deployment information.

7. The forest growth dynamic analysis system based on DBH monitoring ring according to claim 6 is characterized in that: The formula for calculating the compressed deployment spacing value of each point is as follows: ； in, Indicates the The compressed deployment spacing value of each monitoring point, Indicates the The original deployment spacing of the points, Indicates the The average annual growth rate of DBH between a point and its adjacent points, Indicates the The normalized value of the maximum horizontal projection diameter of the canopy corresponding to each point, Indicates the The normalized value of the vertical height from the maximum radial expansion point at the base of the trunk to the ground, Indicates the The standard spacing compression adjustment reference value corresponding to each point.

8. The forest growth dynamic analysis system based on the diameter at breast height monitoring ring according to claim 7 is characterized in that: The factor weighting module includes: The light trigger identification submodule calls the three-dimensional coordinate value of each monitoring point based on the spatial deployment information of the monitoring ring, collects the monitoring data of the canopy light sensor within a continuous time period according to the coordinate position, and determines whether it is in a continuous low light state according to the set light intensity threshold. If it meets the threshold, the point weight adjustment trigger flag is set to obtain the light trigger state label set; The shading weight construction submodule collects the canopy shading rate values ​​of the corresponding points based on the light trigger state label set, extracts the change trend of the light monitoring sequence in chronological order, calculates the relative proportion of the shading rate in the model factor according to the set light trend benchmark, obtains the proportional distribution value of the light factor at each point, and obtains the light factor weight distribution value set; The humidity weight adjustment submodule calls the light factor weight distribution value set, collects the volume moisture content value recorded by the soil moisture sensor corresponding to the point in a continuous period, and determines whether it exceeds the moisture content threshold. If it exceeds, the difference between the xylem expansion rate and the phloem contraction rate in the diameter at breast height monitoring data is extracted, and the light weight value is deducted or adjusted according to the set rules based on the moisture status to obtain the weighted distribution table of the modeling factors.

9. The forest growth dynamic analysis system based on DBH monitoring ring according to claim 8 is characterized in that: The system further comprises: The trend dynamic analysis module is based on the weighted distribution table of modeling factors, and according to the weight structure of each monitoring point, a weighted combination is performed under a unified time window according to the corresponding weight coefficient. A curve group reflecting the dynamic change trend of the diameter at breast height at each point is constructed, and each group of curves is bound to a spatial position to construct a diameter at breast height change data structure with time continuity and spatial positioning capabilities, thereby obtaining the results of forest diameter at breast height evolution analysis; The forest DBH evolution analysis results include spatial location node index, weighted time series group and DBH response trajectory structure.

10. The forest growth dynamic analysis system based on DBH monitoring ring according to claim 9 is characterized in that: The trend dynamic analysis module includes: The weighted combination submodule extracts the DBH change sequence, canopy light monitoring sequence, and soil moisture monitoring sequence recorded at the point during the entire monitoring period based on the weighted distribution table of the modeling factors, and performs weighted combination processing within a unified time window according to the corresponding weight coefficients to generate a weighted data combination sequence; The time curve generation submodule constructs a data sequence reflecting the dynamic change trend of the diameter at breast height according to the weighted data combination sequence in chronological order, judges the stage continuity characteristics in combination with the change rate trend, identifies the boundaries of the time periods in the sequence and splices them into a time-continuous curve expression to obtain the dynamic trend curve group of the diameter at breast height; The spatial binding construction submodule is based on the dynamic trend curve group of the diameter at breast height, calls the three-dimensional coordinate data of the point in the spatial deployment information of the monitoring ring, binds the dynamic trend curve group of the diameter at breast height with the three-dimensional spatial coordinates according to the point correspondence, and combines them into a data set with a dual structure of time series and spatial index to establish the forest diameter at breast height evolution analysis results.

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