Methods for increasing carbon sequestration in target areas

Abstract

The present invention provides a method for increasing carbon sequestration in a target area, which can be applied to the field of carbon sequestration calculation technology. The method includes: obtaining growth status data of multiple deep-rooted plants in a target area, wherein the multiple deep-rooted plants are cultivated according to preset thinning interval parameters and are different tree species that meet drought resistance conditions; determining plant growth and spatial structure evaluation data of the target area based on the growth status data, wherein the spatial structure evaluation data is used to characterize the spatial hierarchical distribution state of the deep-rooted plants; when the plant growth is less than a preset growth threshold and the spatial structure evaluation data is less than the preset spatial structure evaluation threshold, processing the growth status data using a thinning simulation model to determine a target thinning strategy; and obtaining carbon sequestration based on the carbon conversion coefficient and the growth status data of the target deep-rooted plants in the target area.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon sink calculation, and in particular to a method for improving carbon sink in a target area. Background Art

[0002] With global climate change and intensified human activities, forest ecosystems face severe challenges from habitat degradation and declining carbon sequestration. As a key component of forest resources, plantations offer enormous potential for ecological restoration and carbon sequestration enhancement. However, traditional plantation tending practices often focus on timber production while neglecting ecological benefits. This has led to monotonous stand structure, decreased biodiversity, and declining soil fertility, further weakening the forest's carbon sequestration capacity. Summary of the Invention

[0003] In view of the above problems, the present invention provides a method for increasing carbon sequestration in a target area.

[0004] According to a first aspect of the present invention, there is provided a method for improving carbon sequestration in a target area, comprising: obtaining growth status data of a plurality of deep-rooted plants in a target area, wherein the plurality of deep-rooted plants are cultivated according to preset thinning interval parameters, and the plurality of deep-rooted plants are different tree species that meet drought resistance conditions; determining plant growth and spatial structure evaluation data of the target area based on the growth status data, wherein the spatial structure evaluation data is used to characterize the spatial hierarchical distribution state of the deep-rooted plants; when the plant growth is less than a preset growth threshold and the spatial structure evaluation data is less than the preset spatial structure evaluation threshold, processing the growth status data using a thinning simulation model to determine a target thinning strategy; obtaining carbon sequestration based on the carbon conversion coefficient and the growth status data of the target deep-rooted plants in the target area, wherein the target deep-rooted plants are obtained after thinning the plurality of deep-rooted plants based on the target thinning strategy.

[0005] Optionally, using a thinning simulation model to process growth status data to determine a target thinning strategy includes: determining an upper threshold value of thinning amount based on the growth status data; determining multiple uniform thinning sub-strategies and multiple non-uniform thinning sub-strategies based on the thinning pattern, the number of simulations and the upper threshold value of thinning amount; performing thinning simulations on deep-rooted plants in the target area based on the multiple uniform thinning sub-strategies, and determining first spatial structure evaluation data corresponding to each of the multiple uniform thinning sub-strategies; performing thinning simulations on deep-rooted plants in the target area based on the multiple non-uniform thinning sub-strategies, and determining second spatial structure evaluation data corresponding to each of the multiple non-uniform thinning sub-strategies; determining target spatial structure evaluation data from the multiple first spatial structure evaluation data and the multiple second spatial structure evaluation data; and determining the thinning sub-strategy corresponding to the target spatial structure evaluation data as the target thinning strategy.

[0006] Optionally, the growth status data includes breast diameter data; wherein, based on the thinning mode, the number of simulations and the upper limit threshold of thinning amount, determining multiple uniform thinning sub-strategies includes: when the thinning mode is the uniform thinning mode, determining multiple grid division parameters for the target area according to the boundary of the target area, the multiple grid division parameters are used to divide the target area into multiple grids; determining the thinning amount parameters corresponding to each of the multiple grids according to the upper limit threshold of thinning amount, the multiple grid division parameters and the breast diameter data of deep-rooted plants in each grid; determining multiple uniform thinning sub-strategies according to the multiple grid division parameters and the thinning amount parameters corresponding to each of the multiple grid division parameters.

[0007] Optionally, the growth status data is obtained by collecting data on multiple deep-rooted plants planted based on a mixed planting strategy in the target area. The mixed planting strategy includes at least one of the initial planting density, mixed planting pattern, mixed planting ratio, and mixed planting spacing. The deep-rooted plants include at least one of Chinese pine, Caragana, Platycladus orientalis, Apricot, Hippophae rhamnoides, and Robinia pseudoacacia. The initial planting diameter at breast height of the deep-rooted plants is ≥15cm, and the initial planting crown width is ≥3m.

[0008] Optionally, the mixed planting mode is a strip mixed planting mode, and the width parameter of the transformation zone in the strip mixed planting mode is 15~25m.

[0009] Optionally, the mixed planting pattern is a mixed planting pattern based on a first deep-rooted plant and a second deep-rooted plant, wherein the deep-rooted plants include the first deep-rooted plant and the second deep-rooted plant, the carbon sequestration capacity of the first deep-rooted plant is higher than that of the second deep-rooted plant, and the mixed planting pattern is characterized by a ratio between the number of the first deep-rooted plant and the number of the deep-rooted plant being ≤30%.

[0010] Optionally, the deep-rooted plants include at least one of the following: Chinese pine, Caragana korshinskii, Oriental arborvitae, Prunus armeniaca, Hippophae rhamnoides, and Robinia pseudoacacia.

[0011] Optionally, the number of dead standing trees retained in the target area is 5 to 10 trees per hectare, and the number of fallen trees retained is 10 to 15 trees per hectare.

[0012] Optionally, forest windows and edge retention buffer strips can be opened in the target area, where the spacing between forest windows is 30-50m and the width of edge retention buffer strips is ≥5m.

[0013] Optional methods to increase carbon sequestration in target areas include: adjusting the rotation period to extend by 1 to 2 years when the average annual growth rate of the diameter at breast height of deep-rooted plants is ≥ 1 cm.

[0014] Optionally, determining spatial structure evaluation data based on growth status data includes: for each deep-rooted plant of a plurality of deep-rooted plants, determining status data corresponding to a unit spatial structure formed by each deep-rooted plant and adjacent plants based on a preset number of adjacent plants and growth status data, and obtaining average status data, wherein the status data characterizes the degree of plant growth distribution within the unit spatial structure; and using a spatial structure evaluation model to process the average status data to obtain spatial structure evaluation data.

[0015] According to the method for improving the carbon sequestration in the target area provided by the present invention, a variety of deep-rooted plants with high drought resistance are selected to break the single forest stand structure and increase tree species diversity. When the growth status data is less than a preset growth threshold and the spatial structure evaluation data is less than a preset spatial structure evaluation threshold, a thinning simulation model is used to simulate the growth status data to determine a target thinning strategy with high adaptability and high timeliness. Thinning based on the target thinning strategy increases the growth rate, optimizes the forest stand density and the spatial hierarchy of the forest stand, increases the understory vegetation coverage and soil organic matter content, thereby increasing the carbon sequestration amount, and effectively overcoming the technical problem of insufficient carbon sequestration function caused by habitat degradation and decline in biodiversity. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other objects, features and advantages of the present invention will become more apparent from the following description of the embodiments of the present invention with reference to the accompanying drawings.

[0017] Figure 1 A flow chart of a method for increasing carbon sequestration in a target area according to an embodiment of the present invention is shown.

[0018] Figure 2 A flowchart of determining a target thinning strategy according to an embodiment of the present invention is shown.

[0019] Figure 3 An example diagram of determining a target thinning strategy according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0020] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of embodiments of the present invention. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concept of the present invention.

[0021] The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. The terms "comprise," "include," etc. used herein indicate the presence of features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0022] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0023] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0024] An embodiment of the present invention provides a method for increasing carbon sequestration in a target area. The method includes: obtaining growth status data of multiple deep-rooted plants in a target area, wherein the multiple deep-rooted plants are cultivated according to preset thinning interval parameters and are different tree species that meet drought resistance conditions; determining plant growth and spatial structure evaluation data of the target area based on the growth status data, wherein the spatial structure evaluation data is used to characterize the spatial hierarchical distribution state of the deep-rooted plants; when the plant growth is less than a preset growth threshold and the spatial structure evaluation data is less than the preset spatial structure evaluation threshold, processing the growth status data using a thinning simulation model to determine a target thinning strategy; and obtaining carbon sequestration based on a carbon conversion coefficient and the growth status data of target deep-rooted plants in the target area, wherein the target deep-rooted plants are obtained by thinning the multiple deep-rooted plants based on the target thinning strategy.

[0025] It should be noted that the sequence numbers of the operations in the following method are only used to indicate the operation for the purpose of description, and should not be regarded as indicating the order in which the operations should be performed. Unless explicitly stated, the method does not need to be performed in the order shown.

[0026] Figure 1 A flow chart of a method for increasing carbon sequestration in a target area according to an embodiment of the present invention is shown.

[0027] like Figure 1 As shown, the method 100 for increasing the carbon sequestration amount in a target area includes operations S110 to S140.

[0028] In operation S110 , growth status data of a plurality of deep-rooted plants in a target area is acquired.

[0029] Optionally, the target area may be an arid area, such as the northwest desert area.

[0030] Optionally, deep-rooted plants represent plants whose roots can grow deep into the soil. Multiple deep-rooted plants are different tree species that meet drought-resistant requirements. Deep-rooted plants are native tree species in the target area that are robust and have strong drought and stress resistance. For example, deep-rooted plants may include Chinese pine, Caragana korshinskii, and Platycladus orientalis.

[0031] Optionally, the plurality of deep-rooted plants are cultivated according to preset thinning interval parameters, and the plurality of deep-rooted plants are thinned based on the preset thinning interval parameters.

[0032] Optionally, before thinning, growth status data of a plurality of deep-rooted plants are obtained. The growth status data may be morphological data, physiological data, biomass data, etc. of the growth of the deep-rooted plants.

[0033] In operation S120, a plant growth amount and spatial structure evaluation data of a target area are determined based on the growth status data.

[0034] Optionally, the growth status data is processed using a growth model to obtain the plant growth amount.

[0035] In one embodiment, the plant growth As shown in formula (1):

[0036] (1);

[0037] Here, K represents the plant's growth limit, a represents a constant related to the plant's initial growth, h represents the growth rate parameter, t represents the time variable, and e represents the base of the natural logarithm. This growth model is a symmetrical S-shaped curve with maximum growth at its peak. It is suitable for systems with limited resources and where growth approaches saturation.

[0038] In one embodiment, the plant growth As shown in formula (2):

[0039] (2);

[0040] Here, h represents the growth rate parameter, m represents the shape parameter (which determines the shape of the curve), and e represents the base of the natural logarithm. This growth model is an asymmetric S-shaped curve that better fits the growth process of different tree species or populations.

[0041] Optionally, spatial structure evaluation data is used to characterize the spatial hierarchical distribution of deep-rooted plants. Spatial structure evaluation data reflects the quality of the stand's spatial structure; a larger spatial structure evaluation data value indicates a more ideal stand's spatial structure.

[0042] In operation S130 , when the plant growth amount is less than a preset growth threshold and the spatial structure evaluation data is less than a preset spatial structure evaluation threshold, the growth state data is processed using a thinning simulation model to determine a target thinning strategy.

[0043] Optionally, if the plant growth is less than a preset growth threshold and the spatial structure evaluation data is less than a preset spatial structure evaluation threshold, it means that the plant growth is slow and the stand spatial structure is not ideal, and a reasonable thinning strategy needs to be adopted for thinning.

[0044] Optionally, the thinning simulation model simulates thinning strategies based on growth status data, and the optimization goal is to maximize the spatial structure evaluation data. When the thinning simulation model achieves the optimization goal, the thinning strategy corresponding to the optimization goal is determined as the target thinning strategy.

[0045] Optionally, the target thinning strategy may include a thinning pattern and a thinning quantity.

[0046] In operation S140, a carbon sink amount is obtained based on the carbon conversion coefficient and growth status data of target deep-rooted plants in the target area.

[0047] Optionally, the target deep-rooted plants are obtained by thinning multiple deep-rooted plants based on a target thinning strategy. After thinning multiple deep-rooted plants based on the target thinning strategy, the target deep-rooted plants are retained in the target area, and growth status data of the target deep-rooted plants is collected at preset carbon sequestration measurement intervals.

[0048] Optionally, the growth status data of the target deep-rooted plants may include the number of tree species, wood density, stock volume, etc.

[0049] In one embodiment, the carbon sink C is as shown in formula (3):

[0050] (3);

[0051] in, represents the area of ​​the target area, V represents the volume of deep-rooted plants per hectare in the target area, n represents the total number of deep-rooted plant species, Characterizes the biomass expansion coefficient of the jth deep-rooted plant in the target area, Characterizes the wood density of the jth deep-rooted plant in the target area, It represents the root-to-shoot ratio of the jth deep-rooted plant, and CF represents the carbon conversion coefficient, which can be taken as 0.5.

[0052] Alternatively, the root-to-shoot ratio can be calculated from the above- and below-ground biomass and the average carbon content rate in the target area.

[0053] Alternatively, carbon sink capacity is the amount of carbon dioxide (CO2) absorbed and stored from the atmosphere.

[0054] Optionally, by selecting a variety of deep-rooted plants with high drought resistance, breaking the single forest stand structure and increasing tree species diversity, when the growth status data is less than the preset growth threshold and the spatial structure evaluation data is less than the preset spatial structure evaluation threshold, the thinning simulation model is used to simulate the growth status data to determine a target thinning strategy with high adaptability and timeliness, thereby thinning based on the target thinning strategy increases the growth rate, optimizes the forest stand density and the spatial hierarchy of the forest stand, increases the understory vegetation cover and soil organic matter content, thereby increasing the carbon sequestration amount, and effectively overcoming the technical problem of insufficient carbon sequestration function caused by habitat degradation and biodiversity decline.

[0055] Figure 2 A flowchart of determining a target thinning strategy according to an embodiment of the present invention is shown.

[0056] like Figure 2 As shown, the process 200 of determining a target thinning strategy includes operations S210 to S260.

[0057] In operation S210, an upper threshold value of thinning amount is determined based on growth status data.

[0058] In operation S220 , a plurality of uniform thinning sub-strategies and a plurality of non-uniform thinning sub-strategies are determined based on the thinning mode, the number of simulations, and the upper threshold of the thinning amount.

[0059] In operation S230 , thinning simulations are performed on deep-rooted plants in the target area based on the multiple uniform thinning sub-strategies, and first spatial structure evaluation data corresponding to each of the multiple uniform thinning sub-strategies is determined.

[0060] In operation S240 , thinning simulations are performed on deep-rooted plants in the target area based on the plurality of non-uniform thinning sub-strategies, and second spatial structure evaluation data corresponding to each of the plurality of non-uniform thinning sub-strategies is determined.

[0061] In operation S250 , target spatial structure evaluation data is determined from the plurality of first spatial structure evaluation data and the plurality of second spatial structure evaluation data.

[0062] In operation S260 , the thinning sub-strategy corresponding to the target spatial structure evaluation data is determined as the target thinning strategy.

[0063] Optionally, the growth status data includes the total number of deep-rooted plants in the target area, and the upper threshold of the thinning amount is the total number of deep-rooted plants.

[0064] Optionally, thinning modes may include uniform thinning mode and random thinning mode. The uniform thinning mode divides the forest land in the target area into grids and determines the thinning amount within each grid for thinning. The random thinning mode randomly selects a certain number of deep-rooted plants for thinning.

[0065] Optionally, the number of simulations can be a random number, for example, 1000 times.

[0066] Optionally, the number of simulations may be a random number or an exhaustive number determined by exhaustively enumerating the thinning amount based on an upper threshold of the thinning amount and the grid area and the target area.

[0067] Optionally, multiple uniform thinning sub-strategies are determined based on the number of exhaustive searches, the uniform thinning pattern, and the upper threshold for the thinning amount. A uniform thinning sub-strategy thins trees using the same thinning amount within each grid. For example, if the number of exhaustive searches is 100, 100 uniform thinning sub-strategies are obtained.

[0068] Optionally, multiple non-uniform thinning sub-strategies are determined based on the random thinning pattern, the number of randomizations, and the upper threshold for the thinning amount. A non-uniform thinning sub-strategy is a strategy for thinning based on a random number of thinnings. For example, if the number of randomizations is 1000, 1000 non-uniform thinning sub-strategies are obtained.

[0069] Optionally, each uniform thinning sub-strategy includes the side length, number and thinning amount of the grids, and thinning simulation of deep-rooted plants in the target area is performed based on the side length, number and thinning amount of each grid to determine the first spatial structure evaluation data corresponding to each uniform thinning sub-strategy.

[0070] Optionally, the first spatial structure evaluation data represents the spatial hierarchical structure distribution state of the target area after thinning simulated based on the uniform thinning sub-strategy.

[0071] Optionally, each non-uniform thinning sub-strategy includes a random thinning amount, and a Monte Carlo algorithm is used to simulate thinning of deep-rooted plants in the target area based on the random thinning amount to determine second spatial structure evaluation data corresponding to the non-uniform thinning sub-strategy. The random thinning amount is less than an upper threshold of the thinning amount.

[0072] Optionally, the second spatial structure evaluation data represents the spatial hierarchical structure distribution state of the target area after thinning simulated based on the non-uniform thinning sub-strategy.

[0073] Optionally, multiple first spatial structure evaluation data are obtained after simulation based on multiple uniform thinning sub-strategies, and multiple second spatial structure evaluation data are obtained after simulation based on multiple non-uniform thinning sub-strategies. The maximum first spatial structure evaluation data is determined from the multiple first spatial structure evaluation data, and the maximum second spatial structure evaluation data is determined from the multiple second spatial structure evaluation data. The maximum value between the maximum first spatial structure evaluation data and the maximum second spatial structure evaluation data is determined as the target spatial structure evaluation data.

[0074] For example, after simulation based on multiple uniform thinning sub-strategies (thinning sub-strategy A1, thinning sub-strategy A2, thinning sub-strategy A3), the corresponding first spatial structure evaluation data (QA1, QA2, QA3) are obtained. After simulation based on multiple non-uniform thinning sub-strategies (thinning sub-strategy B1, thinning sub-strategy B2, thinning sub-strategy B3), the corresponding second spatial structure evaluation data (QB1, QB2, QB3) are obtained. The maximum first spatial structure evaluation data QA2 is determined from QA1, QA2, and QA3, and the maximum second spatial structure evaluation data QB1 is determined from QB1, QB2, and QB3. QA2 is greater than QB1. Therefore, QA2 is determined as the target spatial structure evaluation data, and the thinning sub-strategy A2 corresponding to QA2 is determined as the target thinning strategy.

[0075] Optionally, thinning simulations are performed based on multiple uniform thinning sub-strategies and multiple non-uniform thinning sub-strategies respectively to obtain multiple first spatial structure evaluation data and second spatial structure evaluation data. By comparing the multiple first spatial structure evaluation data and the second spatial structure evaluation data, the maximum value is determined as the target spatial structure evaluation data, thereby achieving the optimization goal of maximizing the spatial structure evaluation data of the thinning simulation, dynamically selecting thinning sub-strategies with high adaptability, effectively optimizing the spatial structure after thinning, promoting the growth rate of target deep-rooted plants, and increasing carbon sequestration.

[0076] Optionally, the growth status data includes breast diameter data; wherein, based on the thinning mode, the number of simulations and the upper limit threshold of thinning amount, determining multiple uniform thinning sub-strategies includes: when the thinning mode is the uniform thinning mode, determining multiple grid division parameters for the target area according to the boundary of the target area, the multiple grid division parameters are used to divide the target area into multiple grids; determining the thinning amount parameters corresponding to each of the multiple grids according to the upper limit threshold of thinning amount, the multiple grid division parameters and the breast diameter data of deep-rooted plants in each grid; determining multiple uniform thinning sub-strategies according to the multiple grid division parameters and the thinning amount parameters corresponding to each of the multiple grid division parameters.

[0077] Optionally, the boundary is the area bounds of the target region.

[0078] Optionally, the grid partition parameter represents the side length of the grid, and the grid is square. Based on the boundary of the target area, multiple grid partition parameters are exhaustively enumerated. The grid partition parameter is smaller than the boundary.

[0079] Optionally, the target area is divided into multiple grids based on the boundary of the target area and grid division parameters.

[0080] Optionally, the thinning amount parameter includes the number of plants to be removed from each grid and the deep-rooted plants to be removed. The thinning amount parameter is less than an upper threshold value of thinning amount.

[0081] For example, the upper threshold of thinning amount is FF plants, the grid division parameter PI is an integer, and the number of grids is determined to be SS according to the boundary of the target area and the grid division parameter PI. It is determined that FF / SS deep-rooted plants need to be removed from each grid. For each grid, multiple deep-rooted plants are sorted in ascending order based on the diameter at breast height data, and deep-rooted plants located from the first position to the FF / SS position are removed.

[0082] Optionally, both the grid division parameter and the thinning amount parameter are variables. After determining a grid division parameter, the corresponding thinning amount parameter is determined to form a uniform thinning sub-strategy. Therefore, multiple uniform thinning sub-strategies are obtained based on the target area boundary, the upper threshold of the thinning amount, and the diameter at breast height data.

[0083] Figure 3 An example diagram of determining a target thinning strategy according to an embodiment of the present invention is shown.

[0084] like Figure 3As shown, multiple uniform thinning sub-strategies are determined based on the uniform thinning pattern, including: dividing the target area into square grids with a grid division parameter PI (such as a grid side length of PI) according to the boundary of the target area, the upper threshold of the thinning amount is FF plants, and the number of grids is determined to be SS. The grid division parameters and thinning amount parameters are exhaustively enumerated by an exhaustive algorithm to obtain multiple uniform thinning sub-strategies by combination. After simulating multiple uniform thinning sub-strategies (such as sorting multiple deep-rooted plants in ascending order based on the diameter at breast height data in each grid, and removing deep-rooted plants located at the first position to the FF / SS position), the corresponding first spatial structure evaluation data are obtained, and the maximum first spatial structure evaluation data is determined. data; determining multiple non-uniform thinning sub-strategies based on the random thinning pattern includes: repeating the simulation 10,000 times, generating a random thinning amount in each simulation using the Monte Carlo algorithm, the random thinning amount being less than an upper threshold value of the thinning amount, performing thinning simulation on deep-rooted plants in the target area based on the random thinning amount, determining multiple second spatial structure evaluation data corresponding to each of the multiple non-uniform thinning sub-strategies, and determining the maximum second spatial structure evaluation data; determining the maximum value between the maximum first spatial structure evaluation data and the maximum second spatial structure evaluation data as the target spatial structure evaluation data, and determining the thinning sub-strategy corresponding to the target spatial structure evaluation data as the target thinning strategy.

[0085] Optionally, the growth status data is obtained by collecting data on multiple deep-rooted plants planted based on a mixed planting strategy in the target area, and the mixed planting strategy includes at least one of the initial planting density, mixed planting pattern, mixed planting ratio, and mixed planting spacing; wherein, the initial planting diameter at breast height of the deep-rooted plants is ≥15cm, and the initial planting crown width is ≥3m, and the deep-rooted plants include at least one of the following: Chinese pine, Caragana korshinskii, Platycladus orientalis, Apricot, Sea buckthorn, and Robinia pseudoacacia.

[0086] Alternatively, a mixed planting strategy may be adopted for multiple deep-rooted plants in the target area, with an initial planting density of 2,000 plants per hectare.

[0087] Optionally, the mixed planting ratio and mixed planting spacing can be determined based on the soil moisture status of the target arid area. For example, the better the soil moisture status, the higher the proportion of deep-rooted plants with strong stress resistance; the better the soil moisture status, the higher the proportion of deep-rooted plants with strong drought tolerance.

[0088] For example, the mixed planting strategy includes Pinus tabulaeformis, Caragana korshinskii, and Platycladus orientalis at a mixed planting ratio of 6:3:1, with a mixed planting spacing of ≥4m.

[0089] Optionally, the mixed planting pattern may be a strip mixed planting pattern or a mixed planting pattern that introduces high carbon sink tree species.

[0090] Alternatively, a mixed planting strategy can be used to plant multiple deep-rooted plants to break up the single stand structure, increase tree species diversity, provide habitats for plants and animals of different ecological niches, and increase the biodiversity index (Shannon-Wiener) from 1.2 to 1.8. The mixed planting strategy can also be used to increase the carbon storage of understory vegetation to 10%-25%. The mixed planting pattern, mixed planting ratio, and mixed planting spacing can be dynamically adjusted according to soil moisture status. Stand density control can also be used to reduce intraspecific competition and promote a 15%-20% increase in the growth rate of target trees.

[0091] Optionally, the mixed planting mode is a strip mixed planting mode, and the width parameter of the transformation zone in the strip mixed planting mode is 15~25m.

[0092] For example, in the strip mixed planting pattern, Pinus tabulaeformis and Caragana korshinskii are planted alternately, and the width parameter of the transformation strip is 20m.

[0093] Optionally, the mixed planting pattern is a mixed planting pattern based on a first deep-rooted plant and a second deep-rooted plant, wherein the deep-rooted plants include the first deep-rooted plant and the second deep-rooted plant, the carbon sequestration capacity of the first deep-rooted plant is higher than that of the second deep-rooted plant, and the mixed planting pattern is characterized by a ratio between the number of the first deep-rooted plant and the number of the deep-rooted plant being ≤30%.

[0094] Optionally, the first deep-rooted plant is a high carbon sink tree species, such as eucalyptus.

[0095] Optionally, the second deep-rooted plant can be a native tree species with strong drought resistance, such as Chinese pine, Caragana korshinskii, Platycladus orientalis, and Prunus armeniaca.

[0096] For example, the ratio between the number of first deep-rooted plants and the number of deep-rooted plants is 25%.

[0097] Optionally, the number of dead standing trees retained in the target area is 5 to 10 trees per hectare, and the number of fallen trees retained is 10 to 15 trees per hectare.

[0098] Optionally, regular removal of dead and fallen trees based on a quantitative indicator of dead and fallen tree retention.

[0099] Alternatively, standing trees are trees that have died but are still standing in their original location, and fallen trees are trees that have died and fallen to the ground.

[0100] For example, the number of dead standing trees to be retained is 8 per hectare, and the number of fallen trees to be retained is 12 per hectare.

[0101] Optionally, based on the quantitative indicators for the retention of dead standing trees / fallen trees, dead standing trees and fallen trees can be retained to provide habitats and food sources for wildlife, promote litter decomposition and nutrient recycling, increase soil organic matter content and carbon storage, and significantly improve soil fertility.

[0102] Optionally, forest windows and edge retention buffer strips can be opened in the target area, where the spacing between forest windows is 30-50m and the width of edge retention buffer strips is ≥5m.

[0103] Alternatively, a 20m x 20m forest window or a 10m x 10m dense forest window can be created for every 5 hectares of planted forest in the target area. The 10m x 10m dense forest window is more suitable for steep slopes.

[0104] Optionally, the gap spacing is 30m.

[0105] Optionally, the size and spacing of forest windows can be dynamically regulated to promote the renewal of positive tree species and increase the vertical structural layers (such as canopy layer → shrub layer → herb layer). The coverage of positive shrubs (such as rhododendrons) in forest windows can be increased to 60%, and carbon storage can be increased by 0.5 tons / hectare.

[0106] Optional methods to increase carbon sequestration in target areas include: adjusting the rotation period to extend by 1 to 2 years when the average annual growth rate of the diameter at breast height of deep-rooted plants is ≥ 1 cm.

[0107] Optionally, the rotation period can be adjusted dynamically. When the average annual growth rate of the diameter at breast height of deep-rooted plants is ≥1 cm, the rotation period can be extended by 1 to 2 years.

[0108] Alternatively, based on a rotation extension strategy, the rotation of deep-rooted plants can be adjusted to 10-15 years based on the average annual growth in DBH.

[0109] For example, the rotation period of Pinus massoniana was adjusted from the initial 8 years to 12 years, and the rotation period of Camphor tree was adjusted from the initial 8 years to 15 years.

[0110] Optionally, dynamically extending the felling rotation period can promote tree growth and increase carbon storage in understory vegetation, reduce periodic carbon storage losses, extend the peak carbon storage period by 3-5 years, make the spatial distribution of carbon storage more balanced, promote synergistic enhancement between the tree layer, understory vegetation, and soil carbon pools, and significantly enhance carbon sequestration capacity.

[0111] Optionally, determining spatial structure evaluation data based on growth status data includes: for each of a plurality of deep-rooted plants, determining status data corresponding to a unit spatial structure formed by each deep-rooted plant and adjacent plants based on a preset number of adjacent plants and growth status data, and obtaining average status data, wherein the status data characterizes the degree of plant growth distribution within the unit spatial structure; and using a spatial structure evaluation model to process the average status data to obtain spatial structure evaluation data.

[0112] Optionally, the preset number of adjacent plants may be 4, and the overall stand spatial structure of the target area is analyzed using a unit spatial structure consisting of each deep-rooted plant and four adjacent plants. The four adjacent plants may be adjacent plants in four directions of the deep-rooted plant.

[0113] Optionally, the state data may reflect the spatial distribution and relationship of different deep-rooted plants, for example, the state data may include intergrowth degree, openness, angular scale, size ratio, and competition index.

[0114] Optionally, the state data corresponding to each deep-rooted plant in the unit space structure is determined, and then the average state data of multiple deep-rooted plants in the target area is determined.

[0115] In one embodiment, deep-rooted plants The mixed degree M is shown in formula (4):

[0116] (4);

[0117] Among them, when deep-rooted plants With adjacent plants When the plants are not of the same species, =1, otherwise =0, Represents the preset number of adjacent plants,

[0118] In one embodiment, deep-rooted plants The openness B is shown in formula (5):

[0119] (5);

[0120] in, Deep-rooted plants With adjacent plants distance, For adjacent plants The tree is tall.

[0121] In one embodiment, deep-rooted plants The angular scale W of is shown in formula (6):

[0122] (6);

[0123] Among them, when adjacent plants The root angle a is smaller than the standard angle hour, =1, otherwise =0, when the number of adjacent plants is preset to 4, the standard angle =72°.

[0124] In one embodiment, deep-rooted plants Size ratio As shown in formula (7):

[0125] (7);

[0126] Among them, when adjacent plants Deep-rooted plants When the volume is large, =1, otherwise 0.

[0127] In one embodiment, deep-rooted plants The competition index Ici is shown in formula (8):

[0128] (8);

[0129] Among them, when adjacent plants The tree is taller than the deep-rooted plants When the tree is tall, , ;otherwise, , =0, Deep-rooted plants The tree is tall, For adjacent plants The tree is tall, For adjacent plants Deep-rooted plants distance between Deep-rooted plants The size ratio of .

[0130] Optionally, the larger the competition index, the more intense the competition.

[0131] Optionally, based on the status data corresponding to each deep-rooted plant in the unit space structure, the average status data of multiple deep-rooted plants in the target area are calculated, for example, the average degree of intermingling, the average openness, the average angular scale, the average size ratio and the average competition index.

[0132] In one embodiment, the spatial structure evaluation model is shown in formula (9):

[0133] (9);

[0134] Among them, Q is the spatial structure evaluation data, 、 、 、 、 They are the average degree of intermixing, average openness, average angular scale, average size ratio and average competition index of the target area. 、 、 、 、 They are the standard deviations of intermixture, openness, angular scale, size ratio and competition index respectively.

[0135] Optionally, the angular scale is processed by subtracting 0.5 from all the angular scale data at the same time and taking the absolute value so that the angular scale value range becomes (0, 0.5], and the optimal value is the minimum value close to 0.

[0136] Optionally, the spatial structure evaluation model can be used to process the average state data to obtain spatial structure evaluation data. Using multiplication and division, the ascending state data are multiplied and the descending state data are divided to construct the spatial structure evaluation model to optimize the objective function. The spatial structure evaluation data Q reflects the quality of the spatial structure, emphasizing that the optimal spatial structure is the optimal overall goal. A larger Q indicates a more ideal stand spatial structure.

[0137] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the boxes may occur in an order different from that marked in the accompanying drawings. For example, two boxes shown in succession may actually be executed substantially in parallel, or they may sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, as well as the combination of boxes in the block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or may be implemented using a combination of dedicated hardware and computer instructions. It will be understood by those skilled in the art that the features described in the various embodiments of the present invention may be combined and / or coupled in various ways, even if such combinations or couplings are not explicitly described in the present invention. In particular, the features described in the various embodiments of the present invention may be combined and / or coupled in various ways without departing from the spirit and teachings of the present invention. All such combinations and / or combinations fall within the scope of the present invention.

[0138] The above describes embodiments of the present invention. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present invention. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be advantageously used in combination. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. A method for increasing carbon sequestration in a target area, characterized in that: The method comprises: Acquiring growth status data of a plurality of deep-rooted plants in a target area, wherein the plurality of deep-rooted plants are cultivated according to preset thinning interval parameters and are different tree species that meet drought resistance conditions; Determining plant growth amount and spatial structure evaluation data of the target area according to the growth status data, wherein the spatial structure evaluation data is used to characterize the spatial hierarchical structure distribution state of the deep-rooted plants; When the plant growth amount is less than a preset growth threshold and the spatial structure evaluation data is less than a preset spatial structure evaluation threshold, processing the growth state data using a thinning simulation model to determine a target thinning strategy includes: determining an upper threshold value of thinning amount according to the growth status data; Determining a plurality of uniform thinning sub-strategies and a plurality of non-uniform thinning sub-strategies based on the thinning pattern, the number of simulations, and the upper threshold value of the thinning amount; performing thinning simulations on the deep-rooted plants in the target area based on the plurality of uniform thinning sub-strategies, and determining first spatial structure evaluation data corresponding to each of the plurality of uniform thinning sub-strategies; performing thinning simulations on the deep-rooted plants in the target area based on the plurality of non-uniform thinning sub-strategies, and determining second spatial structure evaluation data corresponding to each of the plurality of non-uniform thinning sub-strategies; determining target spatial structure evaluation data from a plurality of first spatial structure evaluation data and a plurality of second spatial structure evaluation data; determining a thinning sub-strategy corresponding to the target spatial structure evaluation data as a target thinning strategy; The carbon sink amount is obtained according to the carbon conversion coefficient and the growth status data of the target deep-rooted plants in the target area, wherein the target deep-rooted plants are obtained by thinning a plurality of the deep-rooted plants based on the target thinning strategy.

2. The method according to claim 1, characterized in that The growth status data includes diameter at breast height data; The step of determining a plurality of uniform thinning sub-strategies based on the thinning pattern, the number of simulations, and the upper threshold of the thinning amount includes: In a case where the thinning mode is a uniform thinning mode, determining a plurality of grid division parameters for the target area according to a boundary of the target area, the plurality of grid division parameters being used to divide the target area into a plurality of grids; Determining thinning amount parameters corresponding to each of the plurality of grids according to the thinning amount upper threshold, the plurality of grid division parameters, and the diameter at breast height data of the deep-rooted plants in each of the grids; A plurality of uniform thinning sub-strategies are determined according to the plurality of grid division parameters and thinning amount parameters corresponding to the plurality of grid division parameters.

3. The method according to claim 1, characterized in that The growth status data is obtained by collecting data on a plurality of deep-rooted plants planted in the target area based on a mixed planting strategy, wherein the mixed planting strategy includes at least one of initial planting density, mixed planting pattern, mixed planting ratio, and mixed planting spacing, and the deep-rooted plants include at least one of Chinese pine, Caragana korshinskii, Platycladus orientalis, Apricot korshinskii, Sea buckthorn, and Robinia pseudoacacia, and the initial planting diameter at breast height of the deep-rooted plants is ≥15 cm, and the initial planting crown width is ≥3 m.

4. The method according to claim 3, characterized in that The mixed planting mode is a strip mixed planting mode, and the width parameter of the transformation zone used in the strip mixed planting mode is 15~25m.

5. The method according to claim 3, characterized in that The mixed planting pattern is a mixed planting pattern based on a first deep-rooted plant and a second deep-rooted plant, wherein the deep-rooted plants include the first deep-rooted plant and the second deep-rooted plant, the carbon sequestration capacity of the first deep-rooted plant is higher than that of the second deep-rooted plant, and the mixed planting pattern is characterized by a ratio between the number of the first deep-rooted plant and the number of the deep-rooted plant being ≤30%.

6. The method according to claim 1, characterized in that The number of dead standing trees retained in the target area is 5 to 10 trees per hectare, and the number of fallen trees retained is 10 to 15 trees per hectare.

7. The method according to claim 1, characterized in that Forest windows and edge retention buffer zones are opened in the target area, wherein the spacing between the forest windows is 30-50m, and the width of the edge retention buffer zone is ≥5m.

8. The method according to claim 1, characterized in that The method comprises: When the average annual growth rate of the diameter at breast height of the deep-rooted plants is ≥1 cm, the extension period of the adjusted rotation period is in the range of 1 to 2 years.

9. The method according to claim 1, characterized in that Determining the spatial structure evaluation data according to the growth status data includes: For each of the plurality of deep-rooted plants, determining status data corresponding to a unit space structure formed by each of the deep-rooted plants and the adjacent plants based on a preset number of adjacent plants and the growth status data, to obtain average status data, wherein the status data represents a degree of plant growth distribution within the unit space structure; The spatial structure evaluation data is obtained by processing the average state data using a spatial structure evaluation model.

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