Single plant afforestation carbon sink metering and monitoring method based on machine vision recognition

Abstract

The invention discloses a single plant afforestation carbon sink metering and monitoring method based on machine vision recognition, and belongs to the technical field of geographic spatial data processing, and the method comprises the steps: S10, obtaining spatial distribution data of a preset monitoring sample plot, the spatial distribution data comprising forest types and forest distribution; monitoring deployment is carried out in a preset monitoring sample plot, and the monitoring deployment mode comprises the step of arranging a plurality of observation cameras in the preset monitoring sample plot; and step S20, video picture data of the forest is acquired based on the arranged observation camera so as to acquire growth data information of the forest, and the growth data information comprises the height, the diameter and the coverage of the forest. According to the invention, through a machine vision identification technology, effective monitoring of forest tree growth data is realized, accuracy and efficiency of carbon sink metering are improved, and through periodic recording and analysis of growth data information, a change rule of forest tree growth can be found in time, and a scientific basis is provided for dynamic monitoring of carbon sink.

Description

Technical Field

[0001] The present invention relates to the technical field of geospatial data processing, and particularly to a method for single-tree afforestation carbon sink measurement and monitoring based on machine vision recognition. Background Art

[0002] Carbon sink afforestation refers to an afforestation activity with special requirements carried out on land where the baseline has been determined, with the main purpose of increasing carbon sinks, and implementing carbon sink measurement and monitoring during the afforestation and the growth process of its forest stand (trees).

[0004] Carbon sink measurement and monitoring are the key technical links in carbon sink afforestation. It is necessary to accurately measure the carbon storage before and after afforestation and continuously monitor the carbon absorption during the forest growth process. Common measurement methods include plot surveys, remote sensing technology, and biomass models, etc.

[0005] To promote the development of carbon sink measurement and monitoring technologies, the application document with the technical application number CN202310203806.X provides a method for dynamic carbon sink measurement in carbon sink afforestation projects. This technical solution includes the design and layout of Internet of Things (IoT) monitoring plots, automatic data collection in IoT plots; setting the data collection frequency in IoT plots, and completing the tasks of parsing and storing the collected data; and monitoring the forest carbon storage in carbon sink afforestation, including successively calculating the carbon storage in the monitoring area, calculating and verifying the accuracy of the carbon storage in the monitoring area, and deducting and correcting the accuracy of the carbon storage in the monitoring area. This technical solution reduces the monitoring cost and investigation errors by deploying IoT monitoring plots and automatically obtaining monitoring data at fixed intervals to calculate the forest carbon storage, and at the same time improves the timeliness of carbon sink measurement and monitoring.

[0006] However, in actual situations, forestry projects have a long growth cycle and are extremely significantly affected by natural factors. Climate change, such as droughts, floods, extreme temperature and other disaster weather events, will cause serious damage to forest growth, and thus affect the accumulation of carbon sink amounts. For example, a long-term drought will cause the growth of trees to slow down or even die, and forest fires will instantly release a large amount of stored carbon, resulting in the loss of carbon sink functions. This leads to the fact that carbon sink measurement and monitoring need to rely on complex models and a large amount of data support. And in the above technical solution, when conducting a per-tree survey, to a large extent, it only relies on the obtained tree diameter data and is executed by reusing calculation software to reduce the probability of data recording and calculation errors during the carbon sink measurement process. It does not consider the fallen trees caused by various reasons. Therefore, its data basis is poor, and it is difficult to extract key measurement parameter information such as tree height, tree diameter, and coverage. When the existing monitoring technology sets a sufficient number of plots in large-area forests for on-site monitoring, this will cause errors in the later verification process of measurement data and it is difficult to ensure the authenticity and reliability of the monitoring data. Summary of the Invention

[0007] In view of the above problems existing in the existing geospatial data processing technology field, the present invention is proposed.

[0008] Therefore, one of the objectives of the present invention is to provide a method for measuring and monitoring the carbon sink of single-tree afforestation based on machine vision recognition. Through machine vision recognition technology, it realizes the effective monitoring of forest growth data, improves the accuracy and efficiency of carbon sink measurement, and can timely discover the changing laws of forest growth by recording and analyzing growth data information in different periods, providing a scientific basis for the dynamic monitoring of carbon sink.

[0009] To solve the above technical problems, the present invention provides the following technical solutions:

[0010] The present invention provides a method for measuring and monitoring the carbon sink of single-tree afforestation based on machine vision recognition, including the following steps:

[0011] Step S10: Obtain the spatial distribution data of a preset monitoring plot, where the spatial distribution data includes tree species and tree distribution; and conduct monitoring deployment in the preset monitoring plot, and the monitoring deployment method includes arranging a number of observation cameras in the preset monitoring plot;

[0012] Step S20: Obtain video picture data of trees based on the arranged observation cameras to obtain forest growth data information, where the growth data information includes tree height, tree diameter, and coverage;

[0013] Step S30: Record the growth data information in different periods, calculate the carbon sink based on the recorded growth data information, and simultaneously calculate the single-tree carbon sink of the preset monitoring plot based on the calculation result; the recording of the growth data information in different periods includes recording in a cycle of 1 to 2 years;

[0014] Step S40: Analyze the regular changes of the growth data information in each period, calculate the difference changes in tree height, tree diameter, and coverage of the trees based on the regular changes; at the same time, calculate the quantity difference of the carbon sink based on the difference changes to obtain the regular influence of the difference changes on the quantity difference, and generate a database;

[0015] Step S50: When alternating between periods, divide the coverage of the preset monitoring plot based on the previous period; among them, the division method includes dividing according to the coverage of arbor forests, shrub forests, and bamboo forests, analyzing the changes in the growth data information of different coverages, and the influence changes of the growth data information changes on the carbon sink;

[0016] Step S60: Count the number of forest trees with different coverage degrees, and based on this number, distinguish the tree height and tree diameter of the forest trees in the growth data information into intervals, obtain the regular changes of the carbon sequestration amounts of the forest trees in different intervals, upload the regular changes to the database, and update the database.

[0017] As a preferred embodiment of the present invention, wherein: in the step S60, it includes distinguishing the tree height and tree diameter of the forest trees according to θ 1 区间 , θ 2 区间 and θ n 区间 , where n represents the nth interval; and calculate the difference in carbon sequestration amounts of the forest trees in different intervals in the previous cycle, which is calculated according to the following formula:

[0018] where, δ i represents the δth carbon sequestration amount obtained in the i-th cycle;

[0019] In the formula, C tree represents the total weight of the forest trees in different intervals, with the unit of ton; △ tree represents the average tree height and tree diameter of the forest trees in different intervals; t1 and t2 represent the start time and end time of the calculation.

[0020] As a preferred embodiment of the present invention, wherein: according to the calculation result, divide the corresponding cycle into at least four measurement time periods, and obtain the highest carbon sequestration amount generated in different measurement time periods; based on the atmospheric fluctuations of the preset monitoring sample plot according to the highest carbon sequestration amount, the atmospheric fluctuations include wind speed; use the eddy covariance method to calculate the regular characteristics of the fluctuations of the highest carbon sequestration amount in the same measurement time period under the same interval conditions, which is calculated according to the following formula:

[0021] where, λ represents the time for collecting the fluctuations of the highest carbon sequestration amount;

[0022] In the formula, w o represents the wth carbon sequestration amount collected for the oth time among the forest trees in each same interval; q n represents the qth carbon sequestration amount fluctuation value collected among the forest trees in the nth same interval; k represents the wind speed fluctuation collected in the vertical direction of the forest trees in the same interval.

[0023] As a preferred embodiment of the present invention, in the same interval within different measurement periods, at least 10 historical maximum carbon sink amounts generated are sorted in size, and based on the sorted carbon sink amount data, the size changes of the tree height and tree diameter of the forest trees are analyzed, and data collection is performed; according to the collected data, the data difference between the tree height and tree diameter data of the forest trees within the same interval is calculated, and the data difference is marked as the reference data difference. If the reference data difference shows an increasing trend and the carbon sink amount increases within the corresponding measurement period, it is determined that the calculated value of the carbon sink amount is an outlier; otherwise, it is not determined.

[0024] As a preferred embodiment of the present invention, when it is determined that the calculated value of the carbon sink amount is an outlier, a set of carbon sink amount data is collected to generate a data set, and within the corresponding interval, according to the calculated reference data difference, the forest tree coverage area within this interval is divided into high coverage area, medium coverage area, and low coverage area, and the carbon sink amounts of the forest trees with the largest tree height and tree diameter in the top one-third of different coverage areas are calculated separately, and the calculated values are marked as reference values; if the calculated value of the carbon sink amount of the forest trees in a future period exceeds the reference value, it is determined that the calculated value of the carbon sink amount is a normal value; otherwise, it is not determined.

[0025] As a preferred embodiment of the present invention, among the forest trees with the largest tree height and tree diameter in the top one-third, the proportion of these forest trees in all the forest trees in the corresponding coverage area is calculated, and based on the proportion, the number of these forest trees is obtained. The numbers are sorted in size, and a critical threshold is preset according to the carbon sink amount difference; when calculating the carbon sink amount based on each of these numbers in a future period, if the carbon sink amount difference exceeds the critical threshold, it is determined that the calculated value of the carbon sink amount is an outlier; otherwise, it is not determined.

[0026] As a preferred embodiment of the present invention, the carbon sink amount when exceeding the critical threshold is recorded and stored, the average carbon sink amount of the forest trees is calculated according to the carbon sink amount, and the carbon sink amount of a single forest tree among these numbers of forest trees is calculated according to the average carbon sink amount. If the carbon sink amount of a certain forest tree is lower than the average carbon sink amount, the carbon sink amount generated by this forest tree is marked as the key monitored carbon sink amount.

[0027] As a preferred embodiment of the present invention, the number of monitoring times is preset according to the change of the carbon sink amount generated by the forest trees, and the carbon sink amount corresponding to each monitoring is recorded. The change trend of the carbon sink amount is analyzed, and an ROC curve is drawn; at the same time, the preset number of monitoring times is given as a monitoring period. If the change trend shows a downward trend within the monitoring period, it is determined that the forest trees are in an abnormal growth state and are removed from the monitoring objects.

[0028] A terminal includes a processor, an input interface, an output interface, and a memory. The processor, input interface, output interface, and memory are interconnected. Among them, the memory is used to store a computer program, and the computer program includes program instructions. The processor is configured to call the program instructions to execute a single-tree afforestation carbon sink measurement and monitoring method based on machine vision recognition.

[0029] A computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, the processor is caused to execute a single-tree afforestation carbon sink measurement and monitoring method based on machine vision recognition.

[0030] 1. By deploying observation cameras in a preset monitoring plot, using machine vision technology to obtain the growth data information of forest trees, and recording the growth data information at a given cycle, the changing rules of the growth of forest trees can be captured in a timely manner, providing basic data for the accurate calculation of the carbon sink amount.

[0031] 2. Based on the recorded growth data information, calculating the carbon sink amount per tree for the preset monitoring plot can accurately evaluate the carbon sink contribution of each tree. At the same time, by analyzing the regular changes in the growth data information, calculating the differential changes in the tree height, tree diameter, and coverage of forest trees, and calculating the amount difference of the carbon sink amount based on these differential changes, a database is generated to provide data support for subsequent research.

[0032] 3. When the cycle alternates, dividing the coverage of the preset monitoring plot, analyzing the changes in the growth data information of different coverages and their impact on the carbon sink amount, which helps to achieve classified management and precise policy implementation; and by counting the number of forest trees in different coverages and distinguishing the intervals of the tree height and tree diameter of forest trees, obtaining the regular changes of forest trees in different intervals in the carbon sink amount, further refining the management measures.

[0033] 4. By analyzing the changing trend of the calculated value of the carbon sink amount, combining indicators such as the reference data difference and the historical highest carbon sink amount, abnormal values can be found and eliminated in a timely manner, improving the accuracy and reliability of the carbon sink amount calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. Among them:

[0035] Figure 1 is a schematic flowchart of the method according to the embodiment of the present invention;

[0036] Figure 2 Schematic diagram of the process structure according to an embodiment of the present invention. Specific implementation manner

[0037] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the described embodiments of the present invention fall within the scope of protection of the present invention.

[0038] Due to the shortcomings of the prior art, the data obtained during the calculation of the carbon sink volume is of poor quality, making it difficult to extract key measurement parameter information such as tree height, tree diameter, and coverage. When the existing monitoring technology sets a sufficient number of sample plots in large-scale forests for on-site monitoring, errors may occur during the later verification process of the measurement data, making it difficult to ensure the authenticity and reliability of the monitoring data.

[0039] Based on this, the present invention proposes a method for single-tree afforestation carbon sink measurement and monitoring based on machine vision recognition. Through machine vision recognition technology, it realizes the effective monitoring of forest growth data, improves the accuracy and efficiency of carbon sink volume measurement, and can timely discover the changing rules of forest growth by recording and analyzing growth data information in different periods, providing a scientific basis for the dynamic monitoring of carbon sink volume.

[0040] The following further specifically describes this solution through embodiments in conjunction with the accompanying drawings.

[0041] Referring to Figures 1 to 2 , an embodiment of the present invention, this embodiment provides a method for single-tree afforestation carbon sink measurement and monitoring based on machine vision recognition, including the following steps:

[0042] Step S10: Obtain the spatial distribution data of a preset monitoring sample plot, where the spatial distribution data includes tree species and tree distribution; and perform monitoring deployment in the preset monitoring sample plot. The monitoring deployment method includes arranging a number of observation cameras in the preset monitoring sample plot, and the number of observation cameras is at least 6 or more;

[0043] In this embodiment, specifically how many observation cameras need to be arranged can be reasonably arranged according to the area size of the preset monitoring sample plot and / or the actual number of planted trees. Through this machine vision technology, comprehensive monitoring of the forest trees can be achieved;

[0044] Step S20: Obtain the video picture data of the forest trees based on the arranged observation cameras to obtain the growth data information of the forest trees, where the growth data information includes tree height, tree diameter, and coverage of the forest trees;

[0045] Step S30: Record the growth data information in cycles, calculate the carbon sink amount based on the recorded growth data information, and calculate the carbon sink amount per individual tree for the preset monitoring plot based on the calculation result; recording the growth data information in cycles includes recording in a cycle of 1 to 2 years.

[0046] In this embodiment, the selection of the carbon sink measurement cycle needs to comprehensively consider various factors, including project type, tree growth characteristics, monitoring cost, and data accuracy, etc.; for example, in an afforestation project, fast-growing tree species (such as eucalyptus, poplar, etc.) can reach a certain growth stage within 5 to 7 years, and the change in carbon sink amount is relatively obvious, while slow-growing tree species (such as pine, fir, etc.) may take 10 to 15 years to achieve a better carbon sink effect; therefore, the specific cycle for recording in this embodiment needs to be determined according to the actual situation.

[0047] Step S40: Analyze the regular changes in the growth data information in each cycle, calculate the difference changes in the tree height, tree diameter, and coverage of the forest trees based on the regular changes; at the same time, calculate the quantity difference of the carbon sink amount based on the difference changes to obtain the regular influence of the difference changes on the quantity difference, and generate a database.

[0048] In this embodiment, this step helps to discover abnormal situations in the growth process of forest trees and provides a basis for subsequent monitoring and management.

[0049] Step S50: When alternating cycles, divide the coverage of the preset monitoring plot based on the previous cycle; among them, the division methods include dividing according to the coverage of arbor forests, shrub forests, and bamboo forests, analyzing the changes in the growth data information of different coverages, and the influence changes of the growth data information changes on the carbon sink amount.

[0050] In this embodiment, through the coverage division, different types of forest trees can be managed more precisely.

[0051] Step S60: Count the number of forest trees with different coverages, distinguish the intervals of the tree height and tree diameter of the forest trees in the growth data information based on this number, obtain the regular changes of the forest trees in different intervals on the carbon sink amount, upload the regular changes to the database, and update the database.

[0052] In step S60, it includes distinguishing the tree height and tree diameter of the forest trees according to θ 1 区间 , θ 2 区间 and θ n 区间 , where n represents the nth interval; and calculate the quantity difference of the carbon sink amount of the forest trees in different intervals in the previous cycle, which is calculated according to the following formula:

[0053] where, δ i represents the δth carbon sink volume obtained within the i-th cycle;

[0054] In the formula, C tree represents the total weight of forest trees in different intervals, with the unit of ton; △ tree represents the average tree height and tree diameter of forest trees in different intervals; t1 and t2 represent the start time and end time of the calculation;

[0055] On the above basis, in this embodiment, according to the calculation results, the corresponding cycle is divided into at least four measurement time periods to obtain the highest carbon sink volume generated within different measurement time periods; based on the atmospheric fluctuations of the preset monitoring plot according to the highest carbon sink volume, the atmospheric fluctuations include wind speed; the eddy covariance method is used to calculate the law characteristics of the highest carbon sink volume fluctuation under the same interval within the same measurement time period, and is calculated according to the following formula:

[0056] where, λ represents the time for collecting the highest carbon sink volume fluctuation;

[0057] In the formula, w o represents the wth carbon sink volume collected from the forest trees in the o-th same interval; q n represents the qth carbon sink volume fluctuation value collected from the forest trees in the n-th same interval; k represents the wind speed fluctuation collected in the vertical direction of the forest trees in the same interval;

[0058] In this embodiment, the four measurement time periods are divided by the four seasons; according to the actual forest management experience, seasonal changes have a certain impact on the carbon sink volume, including photosynthesis intensity, temperature and precipitation, as well as vegetation type and growth stage; for example, in summer, the light time is long and the intensity is high, and the photosynthesis of plants is the most vigorous, and the carbon absorption ability is the strongest; while in winter, the light time is short and the intensity is low, and the photosynthesis of plants weakens, and the carbon absorption ability decreases; for example, the atmospheric carbon dioxide concentration is the highest in winter in the Northern Hemisphere. On the one hand, it is because human activities such as heating increase the use of fossil fuels, and on the other hand, it is also related to the weak photosynthesis of plants;

[0059] For another example, trees grow rapidly in spring and summer, and their carbon absorption ability is relatively strong. They enter the dormant period in winter, and their carbon absorption ability decreases; while shrubs and herbaceous plants grow vigorously in spring and summer, and their carbon absorption ability is relatively strong. In autumn and winter, the herbaceous plants wither, and their carbon absorption ability decreases significantly;

[0060] On the above basis, in the same interval within different measurement periods, at least 10 historical maximum carbon sink amounts generated are sorted by size. According to the sorted carbon sink amount data, the size changes of the tree height and tree diameter of the forest trees are analyzed, and data collection is carried out; according to the collected data, the data difference between the tree height and tree diameter data of the forest trees within the same interval is calculated, and the data difference is marked as the reference data difference. If the reference data difference shows an increasing trend and the carbon sink amount increases within the corresponding measurement period, it is determined that the calculated value of the carbon sink amount is an abnormal value; otherwise, it is not determined.

[0061] Further, when it is determined that the calculated value of the carbon sink amount is an abnormal value, a set of carbon sink amount data is collected to generate a data set. Within the corresponding interval, according to the calculated reference data difference, the forest tree coverage area within this interval is divided into high coverage area, medium coverage area, and low coverage area, and the carbon sink amounts of the forest trees with the largest tree height and tree diameter in the top one-third of different coverage areas are calculated separately, and the calculated values are marked as reference values; if the calculated value of the carbon sink amount of the forest trees in the future period exceeds the reference value, it is determined that the calculated value of the carbon sink amount is a normal value; otherwise, it is not determined.

[0062] Meanwhile, among the forest trees with the largest tree height and tree diameter in the top one-third, the proportion of the forest trees in the corresponding coverage area to all the forest trees is calculated, and the number of forest trees is obtained based on the proportion. The numbers are sorted by size, and a critical threshold is preset according to the carbon sink amount difference; when the carbon sink amount is calculated based on each number in the future period, if the carbon sink amount difference exceeds the critical threshold, it is determined that the calculated value of the carbon sink amount is an abnormal value; otherwise, it is not determined.

[0063] In this embodiment, further, the carbon sink amount when exceeding the critical threshold is recorded and stored. The average carbon sink amount of the forest trees is calculated according to the carbon sink amount, and the carbon sink amount of a single forest tree among the number of forest trees calculated according to the average carbon sink amount is calculated. If the carbon sink amount of a certain forest tree is lower than the average carbon sink amount, the carbon sink amount generated by the forest tree is marked as the key monitored carbon sink amount.

[0064] In this embodiment, according to the forest tree management experience, a single tree will also generate carbon sink amount, and the amount of carbon sink amount it generates depends on various factors, including tree species, growth environment, tree age, management measures, etc.

[0065] On the above basis, in this embodiment, the monitoring times are preset according to the change of the carbon sink amount generated by the forest trees, and the carbon sink amounts corresponding to each monitoring are recorded. The change trend of the carbon sink amount is analyzed, and an ROC curve is drawn; at the same time, the preset monitoring times are given as a monitoring period. If the change trend shows a downward trend within the monitoring period, it is determined that the forest trees are in an abnormal growth state and are excluded from the monitoring objects.

[0066] In this embodiment, within the corresponding measurement period, the monitoring times are carried out once every half month.

[0067] A terminal includes a processor, an input interface, an output interface, and a memory. The processor, the input interface, the output interface, and the memory are interconnected. Among them, the memory is used to store a computer program, and the computer program includes program instructions. The processor is configured to call the program instructions to execute a method for single-tree afforestation carbon sink measurement and monitoring based on machine vision recognition.

[0068] A computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, the processor is caused to execute a method for single-tree afforestation carbon sink measurement and monitoring based on machine vision recognition.

[0069] In summary, through machine vision recognition technology, the present invention realizes effective monitoring of forest growth data, improves the accuracy and efficiency of carbon sink measurement, and can timely discover the changing rules of forest growth by recording and analyzing growth data information in different periods, providing a scientific basis for the dynamic monitoring of carbon sink.

[0070] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A method for measuring and monitoring the carbon sink of single-tree afforestation based on machine vision recognition, characterized in that It includes the following steps: Step S10: Obtain the spatial distribution data of the preset monitoring plots, where the spatial distribution data includes tree species and tree distribution; and conduct monitoring deployments in the preset monitoring plots, and the monitoring deployment method includes arranging a number of observation cameras in the preset monitoring plots; Step S20: Obtain the video and picture data of the trees based on the arranged observation cameras to obtain the growth data information of the trees, where the growth data information includes tree height, tree diameter, and coverage; Step S30: Record the growth data information in cycles, and calculate the carbon sink amount based on the recorded growth data information. At the same time, calculate the carbon sink amount per tree in the preset monitoring plots based on the calculation results; the recording of the growth data information in cycles includes recording in cycles of 1 to 2 years; Step S40: Analyze the regular changes in the growth data information in each cycle, calculate the difference changes in tree height, tree diameter, and coverage of the trees based on the regular changes; at the same time, calculate the difference in the carbon sink amount based on the difference changes to obtain the regular influence of the difference changes on the difference in the amount, and generate a database; Step S50: When alternating cycles, divide the coverage of the preset monitoring plots based on the previous cycle; among them, the division method includes dividing according to the coverage of arbor forests, shrub forests, and bamboo forests, analyze the changes in the growth data information of different coverages, and the influence of the changes in the growth data information on the carbon sink amount; Step S60: Count the number of trees with different coverages, and based on this number, distinguish the intervals of the tree height and tree diameter in the growth data information, obtain the regular changes in the carbon sink amount of the trees in different intervals, upload the regular changes to the database, and update the database.

2. The method for measuring and monitoring the carbon sink of single-tree afforestation based on machine vision recognition according to claim 1, characterized in that, In the step S60, it includes distinguishing the tree height and tree diameter of the forest trees according to θ 1 区间 , θ 2 区间 and θ n 区间 for differentiation, where n represents the nth interval; and calculating the difference in carbon sink amounts of forest trees in different intervals in the previous cycle, which is calculated according to the following formula: Among them, δ i represents the δth carbon sink amount obtained in the ith cycle; Where C tree represents the total weight of forest trees in different intervals, with the unit of ton; △ tree represents the average tree height and tree diameter of forest trees in different intervals; t1 and t2 represent the start time and end time of the calculation.

3. The single-tree afforestation carbon sink measurement and monitoring method based on machine vision recognition according to claim 2, characterized in that According to the calculation results, divide the corresponding cycle into at least four measurement time periods, and obtain the highest carbon sink amount generated in different measurement time periods; Based on the highest carbon sink amount and the atmospheric fluctuations of the preset monitoring plots, where the atmospheric fluctuations include wind speed; use the eddy covariance method to calculate the regular characteristics of the fluctuations of the highest carbon sink amount in the same measurement time period and in the same interval according to the following formula: Among them, λ represents the time when the highest carbon sink volume fluctuation is collected; where w o represents the w-th carbon sink amount collected from the forest trees in the o-th same interval; q n represents the q-th carbon sink amount fluctuation value collected from the forest trees in the n-th same interval; k represents the wind speed fluctuation collected in the vertical direction of the forest trees in the same interval.

4. The single-tree afforestation carbon sink measurement and monitoring method based on machine vision recognition according to claim 3, characterized in that Under the same interval conditions in different measurement time periods, sort at least 10 historical highest carbon sink amounts generated, analyze the size changes of the tree height and its tree diameter of the trees based on the sorted carbon sink amount data, and conduct data collection; according to the collected data, calculate the data difference between the tree height and its tree diameter of the trees in the same interval, mark the data difference as the reference data difference, if the reference data difference shows an increasing trend and the carbon sink amount increases in the corresponding measurement time period, then determine that the calculated value of the carbon sink amount is an outlier; Otherwise, do not determine.

5. The method for single-plant forestation carbon sink measurement and monitoring based on machine vision recognition according to claim 4, characterized in that, When it is determined that the calculated carbon sink amount is an abnormal value, collect one set of carbon sink amount data, generate a data set, and within the corresponding interval, divide the forest cover area within the interval into high cover area, medium cover area, and low cover area according to the calculated reference data difference, and calculate the carbon sink amount of the top one-third of the trees with the largest tree height and tree diameter in different cover areas separately, and mark the calculated value as the reference value; If the calculated value of the carbon sink amount of the forest in the future period exceeds the reference value, it is determined that the calculated carbon sink amount is a normal value; Otherwise, it is not determined.

6. The method for single-plant forestation carbon sink measurement and monitoring based on machine vision recognition according to claim 5, characterized in that, Among the top one-third of the trees with the largest tree height and tree diameter, calculate the proportion of the trees in all the trees in the corresponding cover area, obtain the number of the trees based on the proportion, sort them according to the quantity size, and preset a critical threshold according to the carbon sink amount difference; when calculating the carbon sink amount based on each of the number of trees in the future period, if the carbon sink amount difference exceeds the critical threshold, it is determined that the calculated carbon sink amount value is an abnormal value; Otherwise, it is not determined.

7. The method for single-plant forestation carbon sink measurement and monitoring based on machine vision recognition according to claim 6, wherein, Record and store the carbon sink amount when it exceeds the critical threshold, calculate the average carbon sink amount of the trees based on the carbon sink amount, and calculate the carbon sink amount of a single tree among the number of trees based on the average carbon sink amount. If the carbon sink amount of a certain tree is lower than the average carbon sink amount, mark the carbon sink amount generated by the tree as the key monitored carbon sink amount.

8. The method for single-tree afforestation carbon sink measurement and monitoring based on machine vision recognition according to claim 7, characterized in that Preset the number of monitoring times according to the change of the carbon sink amount generated by the tree, record the carbon sink amount corresponding to each monitoring time, analyze the change trend of the carbon sink amount, and draw an ROC curve; at the same time, give the preset number of monitoring times as a monitoring cycle. If the change trend shows a downward trend within the monitoring cycle, it is determined that the tree is in an abnormal growth state and is removed from the monitoring object.

9. A terminal, characterized in that, It includes a processor, an input interface, an output interface, and a memory. The processor, input interface, output interface, and memory are interconnected. Among them, the memory is used to store computer programs, and the computer programs include program instructions. The processor is configured to call the program instructions to execute the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by the processor, the processor is enabled to execute the method according to any one of claims 1 to 8.

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