Method and system for optimizing carbon sequestration and yield increase structure of artificial forest in southern low mountains and hills

Abstract

The invention relates to the technical field of man-made forest management, in particular to a carbon sequestration and yield increase structure optimization method and system for a man-made forest in a southern low mountain and hilly district, and the key point of the technical scheme is that the method comprises the following steps: S1, sample plot data and point cloud data collection, S2, index calculation and trigger judgment, S3, constraint and target input, S4, intelligent decision and scheme generation, and S5, implementation and closed-loop feedback. According to the method, density regulation and control, preferential cut reserving, complementary planting mixing and biological organic fertilizer combined application can be carried out according to different forest stand types, the forest stand structure and age group distribution are reasonably adjusted, the forest stand uniformity and the carbon reserve are improved, meanwhile, the high survival rate of complementary planting seedlings is guaranteed, and the intelligent decision making system and the structure optimization method library are combined; self-adaptive management of forest stand diagnosis, scheme generation, implementation and closed-loop feedback is realized, the carbon sequestration capability and management efficiency of a man-made forest are effectively improved, and the problems of single forest stand, low carbon sequestration efficiency and low management efficiency in the prior art are solved.

Description

Technical Field

[0001] This invention relates to the field of plantation management technology, specifically to a method and system for optimizing the structure of carbon sequestration and yield increase in plantations in low mountain and hilly areas of southern China. Background Technology

[0002] In the low mountain and hilly areas of southern China, plantations are an important type of forest resource, playing a vital role in ecological environmental protection, carbon sequestration capacity enhancement, and economic forest product production. In recent years, with the increasing emphasis placed on carbon neutrality, ecological protection, and sustainable forestry development by the state, the management of plantations not only requires traditional timber production but also needs to consider carbon sequestration capacity and ecological functions. The structural characteristics of plantations, including stand density, age group structure, tree species composition, and spatial pattern, have a significant impact on stand growth, carbon storage, and ecosystem services. Therefore, improving the carbon sequestration capacity and production efficiency of plantations through scientific stand structure optimization management has become an important direction for current forestry technology development.

[0003] However, existing plantation management techniques still have significant shortcomings: for example, traditional forestry surveys rely heavily on manual plot measurements, resulting in long data collection cycles and making it difficult to monitor stand growth in real time. This is especially true for high-density Chinese fir, Masson pine, and moso bamboo forests, where obtaining individual tree characteristics is challenging and accurate assessment of stand structure is impossible. Furthermore, existing measures for stand structure regulation, replanting, and thinning typically rely on experience-based judgments and lack systematic, multi-objective optimization methods, making it difficult to balance carbon sequestration capacity, production benefits, and ecological functions. Moreover, existing technologies often involve one-off management decisions, lacking monitoring and feedback on implementation effects and updates to the methodology library. This leads to unstable management effects under different stands, different ages, and different tree species combinations, making it impossible to form a continuously optimized cyclical management system. To address these issues, we propose a method and system for optimizing the carbon sequestration and production structure of plantations in the low mountain and hilly areas of southern China. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a structural optimization method and system for carbon sequestration and yield enhancement in artificial forests in low mountain and hilly areas of southern China, thus solving the problems mentioned in the background technology.

[0005] The above-mentioned technical objective of the present invention is achieved through the following technical solution: This invention provides a structural optimization method for carbon sequestration and yield enhancement in plantations in low mountain and hilly areas of southern China, characterized by the following steps: S1. Data collection of sample plots and point cloud data: Sample plots are set up every 100-200m along the traversal route of the small plots. The radius of the sample plots is determined according to the national and industry forestry survey standards. In Chinese fir and Masson pine forests, handheld or airborne lidar is used to collect point cloud data. The height of individual trees, crown width and crown elevation are obtained from the point cloud data using known single-tree extraction algorithms. In bamboo forests, sample plots are used to investigate and record the density, age group distribution, culm diameter and shoot withdrawal rate of the sample plots. Soil physicochemical indicators and topographic information of the sample plots are also collected at the same time. S2. Calculation of indicators and determination of triggers: Based on the sample plot data and point cloud data obtained in step S1, calculate the average tree height, total number of trees density, diameter class differentiation degree and age group structure of the forest stand, and compare the calculated indicators with the pre-established site index table or preset threshold to determine whether the trigger conditions for carrying out structural optimization are met. S3. Constraints and Objectives Input: Input operational constraints and objectives, including but not limited to prioritizing carbon sequestration, bamboo shoot production, water conservation, biodiversity enhancement, budget, and seedling availability. S4. Intelligent Decision-Making and Solution Generation: The judgment results of step S2, the sample plot data, and the operational constraints of step S3 are uploaded to the intelligent decision-making system deployed on the server. The system is based on the built-in structural optimization management method library. The matching and optimization engine generates and outputs parameterized structural optimization solutions by reasoning according to rules and multi-objective optimization. The solutions include the proposed mode type, the felling and retention plan, the replanting and reseeding configuration (including tree species, spacing, density, cluster layout and site layout), the fertilization plan, the shrub clearing rules, the sprout removal rules and thinning rules, the implementation sequence, the preliminary cost estimate and the expected technical effect estimate. S5. Implementation and Closed-Loop Feedback: Implement on-site operations according to the output plan of step S4, and collect monitoring data at preset monitoring nodes after implementation and transmit it back to the intelligent decision-making system for applicability correction and matching weight adjustment of the structure optimization operation method library, thereby forming a closed-loop adaptive process of diagnosis, implementation, monitoring and optimization.

[0006] Preferably, the threshold condition for triggering structural optimization in step S2 includes: For Chinese fir or Masson pine forests, the event is triggered when the actual average tree height corresponding to the sample forest age is less than the standard tree height corresponding to the forest age in the site index table, or when the diameter differentiation is less than 0.2 to 0.3, or the natural regeneration rate is less than 10% to 15%. For moso bamboo forests, the event is triggered when the density of timber moso bamboo forests is greater than 3,500 trees per hm⁻² and the proportion of old bamboo is greater than 65%, or the density of bamboo shoot forests is greater than 3,000 trees per hm⁻² and the rate of bamboo shoot withdrawal is greater than 20%, or the total density is less than 1,500 trees per hm⁻².

[0007] Preferably, the triggering determination of the Chinese fir forest or Masson pine forest is preferably in the form of a product of a coefficient k and a standard value of the site index. That is, the triggering is triggered when the actual average tree height is less than k times the standard tree height of the corresponding forest age in the site index table. Here, k is preferably 0.9, and the value range is 0.8 to 0.95. The determination can be combined with the statistical variance of the average tree height and age distribution of the same forest stand. When the variance exceeds 10% to 15%, the value of k can be adjusted by weighting to more accurately reflect the uniformity of the forest stand structure.

[0008] Preferably, the structural optimization scheme generated in step S4 includes one or more of the following modes: Coniferous pure forest structure regulation model: After selective felling, the overall density is controlled at 900-1200 trees / hm⁻², and after replanting, the total stand density is controlled at 1800-2000 trees / hm⁻², with the density of key trees controlled at 25-35 trees / mu. The mixed-layered planting pattern of different ages: the density ratio of the upper layer: middle layer: lower layer is about 1:1:2, and the density of the middle and lower layers is controlled at about 1200 trees·hm⁻², and the canopy closure of the main forest layer is not less than 0.4; Coniferous and broadleaf mixed planting and broadleaf transformation model: Remove coniferous trees with no growth potential or serious pests and diseases, and replant broadleaf trees in batches; Replanting and mixed planting patterns, as well as shrub cutting, sprout removal, and thinning patterns: determine the specific density, configuration, and operation rules based on the matching results.

[0009] Preferably, the replanting and supplementary planting configuration is as follows: Deep-rooted, evergreen broad-leaved and slow-growing tree species are planted in clusters, with 3 to 5 trees per cluster, a spacing of 3×3m between trees and rows, and a spacing of about 8m between clusters. For shallow-rooted, fast-growing, or deciduous broad-leaved trees, random sowing at 2×3m intervals is recommended. The replanted seedlings should be large-cup seedlings that are 2 years or older and have a height of ≥1m. Check the survival rate 60 days after transplanting and replant as needed.

[0010] Preferably, the moso bamboo forest adopts a full life cycle model of "density-driven, young-age carbon sequestration core, and bio-organic fertilizer promoting shoot growth", specifically: The target bamboo density is approximately 2400 plants per hectare. The target age group structure is old: middle-aged: young ≈ 2:3:5; The optimal application rate of bio-organic fertilizer is approximately 1200 kg·hm⁻²·yr⁻¹, applied twice a year in spring and autumn, in conjunction with soil improvement measures, and supplemented by density adjustment measures such as cutting down small plants and leaving large ones, cutting down dense plants and leaving sparse ones, and cutting down inferior plants and leaving superior ones.

[0011] Preferably, the native tree species candidate library in the structure optimization management method library is a structured database, which records tree species name, shade tolerance, root type, water hierarchical utilization, nitrogen fixation or mycorrhizal capacity, complementarity score with the main forest species, seedling availability and applicable land type according to functional tags; The selection of native tree species in the candidate pool includes, but is not limited to: For mixed species of Chinese fir, including camphor tree, privet, red cone, Zhejiang nanmu, Michelia champaca, sassafras, Michelia chapensis, and alder; For mixed species of Masson pine, including Castanopsis fargesii, Schima superba, Quercus acutissima, Castanopsis chinensis, Michelia champaca, Magnolia officinalis, and Phoebe zhennan; For mixed species of bamboo, including Chinese fir, sweetgum, Masson pine, Schima superba, camphor tree, alder, jujube, and sassafras.

[0012] Preferably, the matching and optimization engine uses a multi-objective optimization process based on rule-based reasoning and weighted scoring to screen and rank candidate measures. The scoring factors include functional matching degree, business objective weight, cost-benefit ratio and ecological constraint satisfaction. The engine can output several candidate solutions and rank them according to the comprehensive score. The engine can optionally use linear programming, integer programming or heuristic algorithms as optimization solvers.

[0013] This invention also provides a structural optimization system for carbon sequestration and yield enhancement in plantations in low mountain and hilly areas of southern China, comprising: The system includes a data acquisition module, a point cloud processing module, a stand diagnosis module, a method library database, a matching and optimization module, an output and reporting module, and a monitoring and feedback module. The data acquisition module is used to receive point cloud data, sample plot survey forms, and soil, remote sensing, and meteorological data collected by handheld or airborne lidar. The point cloud processing module is used to denoise, segment the ground, and segment the canopy of point cloud data, and to obtain the height and canopy width of a single tree using a known single tree extraction algorithm; The stand diagnosis module is used to perform trigger judgments based on the site index table or preset thresholds; The matching and optimization module is used to call the method library and generate parameterized construction plans based on the scoring and optimization process; The output and reporting module is used to generate graphical plans, replanting point tables, construction schedules, cost estimates, and expected carbon sequestration benefit reports. The monitoring and feedback module is used to receive post-implementation monitoring data and update the applicability score and matching weight of the method library.

[0014] In summary, the present invention has the following main beneficial effects: This invention combines sample plot survey data with lidar point cloud data and utilizes an intelligent decision-making system and a database of structural optimization management methods to achieve accurate diagnosis and structural optimization of plantations in the low mountain and hilly areas of southern China. Targeting different forest stand types, this invention can enhance the carbon sequestration capacity of forest stands, improve age group and diameter class structure, promote balanced growth, and increase bamboo shoot yield and biodiversity through measures such as selective logging, replanting, thinning, and fertilization.

[0015] This invention can dynamically adjust the scheme based on monitoring data, realize closed-loop adaptive management, reduce human intervention, improve management efficiency and economic benefits, and is applicable to various terrains, soils and forest stand conditions. It has good universality and operability, and provides a scientific and systematic technical means for increasing carbon sequestration and sustainable management of plantations. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0017] 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. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] The following embodiments are used to illustrate the present invention, but should not be used to limit the scope of protection of the present invention. The conditions in the embodiments can be further adjusted according to specific conditions, and simple improvements to the method of the present invention under the premise of the concept of the present invention are all within the scope of protection claimed by the present invention.

[0019] Example 1

[0020] To verify the effect of the method of the present invention on structural optimization and carbon sequestration and yield increase in single-aged Chinese fir forests, a Chinese fir plantation (15-20 years old, average tree height 12-15m, total density of about 1800 trees·hm⁻², single tree species, poor stand uniformity) located in the low mountain and hilly areas of southern China was selected as the test stand. This stand has problems such as average tree height lower than site potential, low diameter class differentiation, and insufficient natural regeneration, which is consistent with the limited carbon sequestration capacity of single-aged coniferous forests.

[0021] Implementation steps: 1. Data collection in sample plots: Set up sample circles every 150m along the forest stand to collect data such as tree height, crown width, crown elevation, tree diameter, and timber growth status; record soil physicochemical indicators (pH, water content, organic matter, nitrogen, phosphorus and potassium content) and topographic information; at the same time, conduct preliminary statistical analysis on the uniformity of forest stand growth to provide a data basis for subsequent trigger determination.

[0022] 2. Indicator Calculation and Trigger Judgment: Based on the collected data, the average tree height, diameter class differentiation, age group structure, and stand homogeneity are calculated. By comparing with the pre-established site index table, the structure optimization operation is triggered when the average tree height is less than 0.9 times the standard value, the diameter class differentiation is less than 0.25, or the stand homogeneity variance exceeds 12%. This judgment, combined with the differences within the stand, makes the scheme more targeted and can optimize for individuals and areas with poor growth.

[0023] 3. Constraints and Target Inputs: Based on the trigger judgment, input the operational constraints and target information, including carbon sequestration priority, biodiversity enhancement, economic cost control, and seedling availability. By comprehensively considering various constraints, the plan can balance ecological benefits and economic feasibility, and avoid the imbalance problem caused by optimizing a single indicator.

[0024] 4. Intelligent Decision-Making and Solution Generation: The intelligent decision-making system uploads the judgment results, sample plot data, and target constraints to the server. Based on the structural optimization management method library, the system generates parameterized structural optimization solutions through rule-based reasoning and multi-objective optimization, including: Selective felling should be carried out, and the overall density after felling should be controlled at 900-1200 trees per hm². After replanting, the total stand density should be 1800-2000 trees per hm², with 25-35 trees per mu being the key trees to be retained. Replanting and reseeding plan: Deep-rooted, evergreen broad-leaved and slow-growing tree species should be planted in clusters, with 3-5 trees per cluster, a spacing of 3×3m between trees and rows, and an interval of about 8m between clusters; shallow-rooted or fast-growing deciduous broad-leaved trees should be randomly sown at intervals of 2×3m; replanting seedlings should be large cup seedlings that are 2 years or older and have a height of ≥1m; check the survival rate 60 days after planting and replant as needed; The rules for shrub clearing, shoot removal, and thinning are executed according to the system matching results; A preliminary cost estimate and a report on the expected carbon sequestration and production increase effects are generated to provide a reference for business decisions.

[0025] 5. Implementation and Closed-Loop Feedback: Conduct on-site management according to the plan. During implementation, record operational data such as tree retention, tree removal, replanting sites, and fertilization amounts. After implementation, collect forest stand growth data, seedling survival rate, and biodiversity indicators at preset monitoring nodes and transmit them back to the system for optimizing the method library and adjusting matching weights to achieve closed-loop adaptive management.

[0026] 6. Experimental Results and Effects: After one year of implementation, the average tree height of the stand increased by about 8%, the diameter class differentiation improved to 0.32, the stand uniformity variance decreased to 8%, the key preserved trees grew healthily, the biodiversity index increased by 2-3 species, and the carbon sequestration capacity was significantly improved. Compared with the control forest without structural optimization, the carbon storage increased by about 12%, verifying the effectiveness of the method of this invention in solving the problems of poor growth uniformity and insufficient carbon sequestration capacity in single-age coniferous forests. This method, through intelligent decision-making system to automatically generate plans, closed-loop feedback and multi-objective optimization, significantly improves the accuracy, operability and sustainability of stand management.

[0027] Example 2

[0028] To verify the effect of the method of the present invention on structural optimization and carbon sequestration and yield increase in heterogeneous multi-layered mixed forests, a Masson pine plantation (20-30 years old, average tree height 15-18m, total density of about 1600 trees·hm⁻², single tree species, and simple stand structure) located in the low mountain and hilly area of ​​southern China was selected as the test stand. This stand has problems such as single canopy layer, excessive canopy closure, insufficient light in the middle and lower layers, and low natural regeneration capacity, which is consistent with the situation of unbalanced ecological function and low carbon sequestration efficiency of coniferous forests.

[0029] Implementation steps: 1. Data collection of sample plots: Sample plots were set up every 120m along the forest stand to collect data on individual tree height, crown width, tree diameter, canopy closure and tree growth status. At the same time, soil physicochemical indicators (organic matter, water content, nutrient content) and topographic information were recorded. By combining sample plot surveys and point cloud data, the density distribution and age group structure of each forest stand level were obtained.

[0030] 2. Indicator Calculation and Trigger Judgment: Calculate the density of the upper, middle, and lower layers of the stand and the total density, and assess the canopy closure, diameter differentiation, and natural regeneration rate. When the stand ratio is unbalanced, the canopy closure of the main stand is below 0.4, the diameter differentiation is below 0.25, or the natural regeneration rate is below 10%, the structure optimization operation is triggered. This judgment, combined with the differences in the stand layers, allows the scheme to regulate the problems of insufficient light and excessive density in the middle and lower layers.

[0031] 3. Constraints and Objectives Input: Input operational constraints and objective information, including carbon sequestration priority, biodiversity enhancement, water conservation, budget and seedling availability, etc. By setting multiple objective weights, the plan can take into account both the optimization of forest stand ecological functions and economic feasibility.

[0032] 4. Intelligent Decision-Making and Solution Generation: The trigger judgment results, sample plot data, and target constraints are uploaded to the intelligent decision-making system to generate parameterized structure optimization solutions, including: The mixed-span planting pattern of different ages is as follows: the density ratio of the upper, middle and lower layers is about 1:1:2, the density of the middle and lower layers is controlled at about 1200 trees per hm⁻², and the canopy closure of the main forest layer is not less than 0.4. Replanting and reseeding plan: Based on the needs of the forest layer, deep-rooted, evergreen broad-leaved, and slow-growing tree species should be planted in clusters in the lower layer, with 3-5 trees per cluster, a plant spacing of 3×3m, and a cluster spacing of about 8m; shallow-rooted, fast-growing, or deciduous broad-leaved trees should be randomly sown in the middle layer and open areas at intervals of 2×3m; replanting seedlings should be large-cup seedlings that are 2 years or older and have a height of ≥1m; check the survival rate 60 days after planting and replant as needed. The rules for shrub clearing, shoot removal, and thinning are executed according to the system matching results, prioritizing the improvement of areas with insufficient sunlight and excessive density. A preliminary cost estimate and a report on the expected carbon sequestration and production increase effects were generated.

[0033] 5. Implementation and Closed-Loop Feedback: On-site operations are carried out according to the plan, including management measures such as stratified thinning, replanting and fertilization. After implementation, forest growth data, canopy closure, seedling survival rate and ecological indicators are collected at preset monitoring nodes and fed back to the system for optimization of the method library and matching weight adjustment to achieve closed-loop adaptive management.

[0034] 6. Experimental Results and Effects: After one year of implementation, the density of the middle and lower layers increased from approximately 900 trees per hm² to approximately 1200 trees per hm², and the stand density was controlled at 1000 trees per hm² after thinning of the upper layer. The overall forest structure became more reasonable, the canopy closure increased from 0.35 to 0.42, the diameter class differentiation increased to 0.33, the natural regeneration rate increased to approximately 15%, the biodiversity index increased by 3-4 species, the stand carbon sequestration capacity was significantly improved, and the carbon storage increased by approximately 14% compared to the unoptimized control forest. This embodiment verifies the effectiveness of the method of the present invention in solving the problems of single forest layer, uneven density, and low carbon sequestration efficiency, while also demonstrating the advantages of intelligent decision-making and closed-loop management.

[0035] Example 3

[0036] To verify the structural optimization and carbon sequestration and yield increase effects of the method of the present invention in the transformation of mixed coniferous and broad-leaved forests, a single Chinese fir forest (25-30 years old, average tree height 16-18m, total density of about 1700 trees·hm⁻²) located in the low mountain and hilly area of ​​southern China was selected as the experimental stand. This stand had problems such as serious coniferous diseases and pests, poor growth potential, low stand uniformity, insufficient light and low natural regeneration rate, which is consistent with the situation of low carbon sequestration efficiency and insufficient biodiversity of single coniferous forests.

[0037] Implementation steps: 1. Data collection in sample plots: Sample plots were set up every 150m along the forest stand to collect data on individual tree height, diameter, crown width, pest and disease status, and forest structure. At the same time, soil physicochemical indicators (organic matter, nutrients, and water content) and topographic information were recorded. Point cloud data were obtained by handheld or airborne lidar to analyze the density, canopy closure, and age group distribution of each forest stand level.

[0038] 2. Indicator Calculation and Trigger Judgment: Calculate the density of the upper, middle and lower layers of the stand and the total density, and assess the canopy closure, diameter differentiation, natural regeneration rate and the proportion of pests and diseases. When the stand is monotypic, the proportion of pests and diseases in the main stand exceeds 15%, the diameter differentiation is less than 0.25 or the natural regeneration rate is less than 10%, the structure optimization operation is triggered.

[0039] 3. Constraints and Objectives Input: Input operational constraints and objective information, including carbon sequestration priority, pest and disease control, biodiversity enhancement, water conservation, budget and seedling availability, so that the generated plan takes into account ecological function, forest health and economic feasibility.

[0040] 4. Intelligent Decision-Making and Solution Generation: The trigger judgment results, sample plot data, and target constraints are uploaded to the intelligent decision-making system to generate parameterized structure optimization solutions, including: Coniferous and broadleaf mixed planting and broadleaf transformation model: Remove coniferous trees that are severely affected by pests and diseases or have low growth potential, and replant broadleaf trees in batches. Replanting and reseeding plan: Deep-rooted, evergreen broad-leaved, and slow-growing tree species should be planted in clusters, with 3-5 trees per cluster, a spacing of 3×3m between trees and rows, and a spacing of about 8m between clusters; shallow-rooted, fast-growing, or deciduous broad-leaved trees should be randomly sown at intervals of 2×3m in open areas; replanting seedlings should be large-cup seedlings that are 2 years or older and ≥1m tall; check the survival rate 60 days after planting and replant as needed. Fertilization plans, shrub clearing, shoot removal, and thinning rules are executed according to the matching results of the intelligent system to ensure the priority growth of broad-leaved trees and improve the forest stand hierarchy. A preliminary cost estimate and a report on the expected carbon sequestration and production increase effects were generated.

[0041] 5. Implementation and Closed-Loop Feedback: Implement the plan on-site in batches to remove diseased and pest-infested coniferous trees, replant broad-leaved trees, and implement forest stand management measures. Collect forest stand growth data, canopy closure, seedling survival rate, disease and pest control effect, and ecological indicators at preset monitoring nodes. The monitoring data is transmitted back to the system to optimize the applicability score of the method library and adjust the matching weights to achieve closed-loop adaptive management.

[0042] 6. Experimental Results and Effects: After one year of implementation, the removal rate of diseased and pest-infested coniferous trees reached 90%, the total stand density was controlled at approximately 1800 trees / hm², the density ratio of the upper, middle, and lower layers was approximately 1:1:2, the canopy closure of the main canopy layer increased to 0.42, the light exposure of the middle and lower layers was significantly improved, the natural regeneration rate increased to approximately 16%, the diameter differentiation increased to 0.34, the survival rate of broad-leaved trees reached over 95%, the biodiversity index increased by 4-5 species, and the carbon sequestration capacity of the stand increased by approximately 18% compared to the unoptimized control forest. This embodiment verifies the effectiveness of the method of the present invention in the transformation of diseased and pest-infested forests, the construction of mixed forests, and multi-objective carbon sequestration and yield increase, while also demonstrating the advantages of closed-loop intelligent management and scheme optimization.

[0043] As can be seen from Examples 1 to 3, the present invention can scientifically regulate stand density, selectively retain and cut down trees, replant mixed trees and apply bio-organic fertilizers for different stand types (single-aged coniferous forests, heterogeneous multi-layered mixed forests and moso bamboo forests), rationally adjust the proportion of each stand level, diameter differentiation degree and age group structure, significantly improve the stand canopy closure, uniformity and carbon storage, while ensuring high survival rate of replanted seedlings and good land and nutrient utilization efficiency. By combining an intelligent decision-making system with a structural optimization management methodology library, this invention achieves full-process adaptive management from stand diagnosis and scheme generation to on-site implementation and closed-loop feedback. This enables multi-objective optimization of forestry management goals (such as carbon sequestration, bamboo shoot production, water conservation, and biodiversity), demonstrating the significant advantages of this invention in enhancing the carbon sequestration capacity of plantations, improving structural balance, and increasing management efficiency. It also effectively solves the problems of single stand type, low carbon sequestration efficiency, and low management efficiency in existing technologies.

[0044] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that, unless otherwise defined, the technical or scientific terms used in this invention should be understood in the ordinary sense by those skilled in the art to which this invention pertains, and the terms "comprising" or "including" or similar terms used in this invention mean that the element or object preceding the word covers the element or object listed after the word and its equivalents.

[0045] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A structural optimization method for carbon sequestration and yield enhancement in artificial forests in low mountain and hilly areas of southern China, characterized in that, Includes the following steps: S1. Data collection of sample plots and point cloud data: Sample plots are set up every 100-200m along the traversal route of the small plots. The radius of the sample plots is determined according to the national and industry forestry survey standards. In Chinese fir and Masson pine forests, handheld or airborne lidar is used to collect point cloud data. The height of individual trees, crown width and crown elevation are obtained from the point cloud data using known single-tree extraction algorithms. In bamboo forests, sample plots are used to investigate and record the density, age group distribution, culm diameter and shoot withdrawal rate of the sample plots. Soil physicochemical indicators and topographic information of the sample plots are also collected at the same time. S2. Calculation of indicators and determination of triggers: Based on the sample plot data and point cloud data obtained in step S1, calculate the average tree height, total number of trees density, diameter class differentiation degree and age group structure of the forest stand, and compare the calculated indicators with the pre-established site index table or preset threshold to determine whether the trigger conditions for carrying out structural optimization are met. S3. Constraints and Objectives Input: Input operational constraints and objectives, including but not limited to carbon sequestration priority, bamboo shoot production priority, water conservation priority, biodiversity enhancement, budget and seedling availability, etc. S4. Intelligent Decision-Making and Solution Generation: The judgment results of step S2, the sample plot data, and the operational constraints of step S3 are uploaded to the intelligent decision-making system deployed on the server. The system is based on the built-in structural optimization management method library. The matching and optimization engine generates and outputs parameterized structural optimization solutions by reasoning according to rules and multi-objective optimization. The solutions include the proposed mode type, the felling and retention plan, the replanting and reseeding configuration (including tree species, spacing, density, cluster layout and site layout), the fertilization plan, the shrub clearing rules, the sprout removal rules and thinning rules, the implementation sequence, the preliminary cost estimate and the expected technical effect estimate. S5. Implementation and Closed-Loop Feedback: Implement on-site operations according to the output plan of step S4, and collect monitoring data at preset monitoring nodes after implementation and transmit it back to the intelligent decision-making system for applicability correction and matching weight adjustment of the structure optimization operation method library, thereby forming a closed-loop adaptive process of diagnosis, implementation, monitoring and optimization.

2. The method according to claim 1, characterized in that, The threshold conditions used to trigger structural optimization in step S2 include: For Chinese fir or Masson pine forests, the event is triggered when the actual average tree height corresponding to the sample forest age is less than the standard tree height corresponding to the forest age in the site index table, or when the diameter differentiation degree or natural regeneration rate is lower than the threshold. For moso bamboo forests, the event is triggered when the density of timber moso bamboo forests is greater than 3,500 trees per hm⁻² and the proportion of old bamboo is greater than 65%, or the density of bamboo shoot forests is greater than 3,000 trees per hm⁻² and the rate of bamboo shoot withdrawal is greater than 20%, or the total density is less than 1,500 trees per hm⁻².

3. The method according to claim 2, characterized in that, The triggering determination for the Chinese fir forest or Masson pine forest is preferably in the form of a product of a coefficient k and a standard value of the site index. That is, it is triggered when the actual average tree height is less than k times the standard tree height of the corresponding forest age in the site index table. Here, k is preferably 0.9, and the value range is 0.8 to 0.

95.

4. The method according to claim 1, characterized in that, The structural optimization scheme generated in step S4 preferably includes one or more of the following modes: Coniferous pure forest structure regulation model: After selective felling, the overall density is controlled at 900-1200 trees / hm⁻², and after replanting, the total stand density is controlled at 1800-2000 trees / hm⁻², with the density of key trees controlled at 25-35 trees / mu. The mixed-layered planting pattern of different ages: the density ratio of the upper layer: middle layer: lower layer is about 1:1:2, and the density of the middle and lower layers is controlled at about 1200 trees·hm⁻², and the canopy closure of the main forest layer is not less than 0.4; Coniferous and broadleaf mixed planting and broadleaf transformation model: Remove coniferous trees with no growth potential or serious pests and diseases, and replant broadleaf trees in batches; Replanting and mixed planting patterns, as well as shrub cutting, sprout removal, and thinning patterns: determine the specific density, configuration, and operation rules based on the matching results.

5. The method according to claim 4, characterized in that, The preferred replanting and supplementary seeding configuration is: Deep-rooted, evergreen broad-leaved and slow-growing tree species are planted in clusters, with 3 to 5 trees per cluster, a spacing of 3×3m between trees and rows, and a spacing of about 8m between clusters. For shallow-rooted, fast-growing, or deciduous broad-leaved trees, random sowing at 2×3m intervals is recommended. The replanted seedlings should be large-cup seedlings that are 2 years or older and have a height of ≥1m. Check the survival rate 60 days after transplanting and replant as needed.

6. The method according to claim 4, characterized in that, The bamboo forest adopts a full life cycle model of "density-driven, young-age carbon sink core, and bio-organic fertilizer to promote shoot growth", specifically: The target bamboo density is approximately 2400 plants per hectare. The target age group structure is old: middle-aged: young ≈ 2:3:5; The optimal application rate of bio-organic fertilizer is approximately 1200 kg·hm⁻²·yr⁻¹, applied twice a year in spring and autumn, in conjunction with soil improvement measures, and supplemented by density adjustment measures such as cutting down small plants and leaving large ones, cutting down dense plants and leaving sparse ones, and cutting down inferior plants and leaving superior ones.

7. The method according to claim 4, characterized in that, The native tree species candidate database in the structure optimization management method library is a structured database that records tree species name, shade tolerance, root system type, water hierarchical utilization, nitrogen fixation or mycorrhizal ability, complementarity score with the main forest species, seedling availability and applicable land type according to functional tags. The selection of native tree species in the candidate pool includes, but is not limited to: For mixed species of Chinese fir, including camphor tree, privet, red cone, Zhejiang nanmu, Michelia champaca, sassafras, Michelia chapensis, and alder; For mixed species of Masson pine, including Castanopsis fargesii, Schima superba, Quercus acutissima, Castanopsis chinensis, Michelia champaca, Magnolia officinalis, and Phoebe zhennan; For mixed species of bamboo, including Chinese fir, sweetgum, Masson pine, Schima superba, camphor tree, alder, jujube, and sassafras.

8. The method according to claim 4, characterized in that, The matching and optimization engine uses a multi-objective optimization process based on rule-based reasoning and weighted scoring to screen and rank candidate measures. The scoring factors include functional matching degree, business objective weight, cost-benefit ratio and ecological constraint satisfaction. The engine can output several candidate solutions and rank them according to the comprehensive score. The engine can optionally use linear programming, integer programming or heuristic algorithms as optimization solvers.

9. A structural optimization system for implementing the method of any one of claims 1 to 8, characterized in that, include: The system includes a data acquisition module, a point cloud processing module, a stand diagnosis module, a method library database, a matching and optimization module, an output and reporting module, and a monitoring and feedback module. The data acquisition module is used to receive point cloud data, sample plot survey forms, and soil, remote sensing, and meteorological data collected by handheld or airborne lidar. The point cloud processing module is used to denoise, segment the ground, and segment the canopy of point cloud data, and to obtain the height and canopy width of a single tree using a known single tree extraction algorithm; The stand diagnosis module is used to perform trigger judgments based on the site index table or preset thresholds; The matching and optimization module is used to call the method library and generate parameterized construction plans based on the scoring and optimization process; The output and reporting module is used to generate graphical plans, replanting point tables, construction schedules, cost estimates, and expected carbon sequestration benefit reports. The monitoring and feedback module is used to receive post-implementation monitoring data and update the applicability score and matching weight of the method library.

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