Forest carbon reserve calculation system and method

Abstract

The invention relates to the technical field of forest resource monitoring, in particular to a forest carbon reserve calculation system and method, and the method comprises the steps: obtaining target data corresponding to a target forest; obtaining forest stand features, climate features and external influence features corresponding to the target forest based on the target data; obtaining forest types, forest stand density, forest stand age groups and soil characteristics based on forest stand characteristics; based on the forest type and the stand density, vegetation carbon reserves are obtained; based on soil characteristics and forest stand age groups, obtaining soil carbon reserves; obtaining a carbon reserve calibration coefficient based on the climate characteristics and the external influence characteristics; and obtaining the forest carbon reserve based on the vegetation carbon reserve, the soil carbon reserve and the carbon reserve calibration coefficient. According to the invention, the calculation precision of the carbon reserves can be improved.

Description

Technical Field

[0001] This application relates to the field of forest resource monitoring technology, and in particular to a forest carbon storage calculation system and method. Background Technology

[0002] Forest carbon storage is a crucial indicator for measuring the carbon sink function of forest ecosystems, and accurate calculation of forest carbon storage is of great significance for addressing climate change and formulating forestry policies. Traditional methods for calculating forest carbon storage mainly rely on sample plot survey data, estimating carbon storage by establishing regression models between biomass and parameters such as tree diameter at breast height (DBH) and tree height. However, this method suffers from problems such as limited data sources, insufficient spatial representativeness, and low computational efficiency, making it difficult to meet the needs for accurate estimation of forest carbon storage over large areas.

[0003] With the development of remote sensing technology, remote sensing image data is widely used in forest carbon storage calculations. However, most current calculation methods based on remote sensing data only consider the spectral characteristics of vegetation, resulting in low calculation accuracy. Therefore, a forest carbon storage calculation method that combines multi-source data is needed to improve calculation accuracy. Summary of the Invention

[0004] To help improve the accuracy of carbon storage calculation, this application provides a forest carbon storage calculation system and method.

[0005] Firstly, this application provides a method for calculating forest carbon storage, which adopts the following technical solution: A method for calculating forest carbon storage includes: Obtain the target data corresponding to the target forest; Based on the target data, obtain the stand characteristics, climate characteristics, and external influence characteristics corresponding to the target forest; Based on the aforementioned stand characteristics, forest type, stand density, stand age group, and soil properties are obtained. Based on the forest type and the stand density, the vegetation carbon storage is obtained; Soil carbon storage was obtained based on the soil characteristics and the stand age group. Based on the aforementioned climate characteristics and external influence characteristics, a carbon storage calibration coefficient is obtained; Forest carbon storage is obtained based on the vegetation carbon storage, the soil carbon storage, and the carbon storage calibration coefficient.

[0006] By adopting the above technical solution, target data of the target forest is first obtained, and forest stand characteristics, climate characteristics, and external influence characteristics are extracted from it. Then, based on the forest stand characteristics, forest type, stand density, stand age group, and soil characteristics are determined, and vegetation carbon storage and soil carbon storage are calculated separately. At the same time, carbon storage calibration coefficients are obtained by combining climate characteristics and external influence characteristics. Finally, forest carbon storage is obtained through vegetation carbon storage, soil carbon storage, and calibration coefficients. By using target data to comprehensively capture the multi-dimensional characteristics of forests, calculating vegetation and soil carbon storage separately and introducing calibration coefficients, it takes into account both the inherent carbon storage characteristics of forests and dynamic factors such as climate and external influences, effectively improving the accuracy and comprehensiveness of forest carbon storage calculation. It can better meet the needs of accurate estimation of forest carbon storage in large areas and provide a more reliable basis for addressing climate change and formulating forestry policies.

[0007] Optionally, obtaining vegetation carbon storage based on the forest type and the stand density includes: Based on the forest type, vegetation information of different vegetation types within the sample area is obtained; Based on the vegetation information, vegetation hierarchical information, tree measurement factors, vegetation density, and vegetation coverage are obtained. The vegetation carbon storage is obtained based on the vegetation hierarchy information, the tree measurement factor, the vegetation density, and the vegetation coverage.

[0008] Optionally, obtaining soil carbon storage based on the soil characteristics and the stand age group includes: Based on the aforementioned soil characteristics, the soil type and soil fertility are determined; Based on the soil fertility and the soil forest age group, the soil biological density was obtained; Soil carbon storage is obtained based on the soil type, soil fertility, and soil biological density.

[0009] Optionally, obtaining the carbon storage calibration coefficient based on the climate characteristics and the external influence characteristics includes: Based on vegetation information and the aforementioned climate characteristics, the degree of climate matching is obtained; Based on the aforementioned climate characteristics, temperature characteristics, humidity characteristics, and disaster characteristics are obtained; Based on the temperature and humidity characteristics, the suitability for vegetation growth is obtained; Based on the climate matching degree and the vegetation growth suitability, the litter generation rate is obtained; Based on the litter generation rate and the stand age group, the litter cover thickness of the target forest is obtained; Based on the disaster characteristics, the thickness of the litter cover, and the external impact characteristics, a carbon storage calibration coefficient is obtained.

[0010] Optionally, obtaining the carbon storage calibration coefficient based on the disaster characteristics, the litter cover thickness, and the external impact characteristics includes: Based on the aforementioned disaster characteristics, the disaster type and the time when the disaster is likely to occur are obtained; Based on the disaster type and the disaster's occurrence time, a first calibration coefficient is obtained; Based on the thickness of the fallen debris cover and the external influence characteristics, a second calibration coefficient is obtained; The carbon storage calibration coefficient is obtained based on the first calibration coefficient and the second calibration coefficient.

[0011] Optionally, obtaining the second calibration coefficient based on the litter cover thickness and the external influence characteristics includes: Based on the aforementioned external influence characteristics, target biological information and human information are obtained; Based on the target biological information, the target biological species and target biological density are obtained; Based on the target organism species and the target organism density, a first influence coefficient on the thickness of the litter cover is obtained; Based on the aforementioned human information, population density, frequency of human activities, and lifestyles are obtained; Based on the population density, the frequency of human activities, and the lifestyle, a second influence coefficient on the thickness of the litter cover is obtained; A second calibration coefficient is obtained based on the thickness of the fallen debris coverage, the first influence coefficient, and the second influence coefficient.

[0012] Optionally, obtaining the second influence coefficient on the thickness of the litter based on the population density, the frequency of human activities, and the lifestyle includes: If the lifestyle described is a dependent lifestyle, then the demand for litter is obtained based on the population density. Based on the demand for fallen debris, an impact coefficient on the thickness of the fallen debris is obtained, and this impact coefficient is used as the second impact coefficient. The formula for calculating the impact coefficient is as follows: Where K1 is the demand impact coefficient, P is the population density, Q is the annual litter demand per unit population, and S is the annual natural litter generation in the target area. μ is the litter regeneration compensation coefficient, and μ is the regional adjustment coefficient. If the lifestyle is a laissez-faire lifestyle, then the activity impact coefficient on the thickness of the litter is obtained based on the population density and the activity frequency, and this activity impact coefficient is used as the second impact coefficient. The calculation formula for the activity impact coefficient is as follows: Where K2 is the activity impact coefficient, Here, F represents the weighting coefficient, and F represents the activity frequency. This is the activity intensity coefficient. is the activity type attenuation coefficient, and T is the anti-interference threshold for litter.

[0013] Optionally, obtaining forest carbon storage based on the vegetation carbon storage, the soil carbon storage, and the carbon storage calibration coefficient includes: Based on the vegetation carbon storage and the soil carbon storage, obtain the comprehensive carbon storage; Forest carbon storage is obtained based on the comprehensive carbon storage and the carbon storage calibration coefficient.

[0014] Secondly, this application also discloses a forest carbon storage calculation system, which adopts the following technical solution: A forest carbon storage calculation system, comprising: The first acquisition module is used to acquire the target data corresponding to the target forest; The second acquisition module is used to acquire the stand characteristics, climate characteristics and external influence characteristics of the target forest based on the target data. The third acquisition module is used to acquire forest type, stand density, stand age group and soil characteristics based on the stand characteristics. The fourth acquisition module is used to acquire vegetation carbon storage based on the forest type and the stand density; The fifth acquisition module is used to acquire soil carbon storage based on the soil characteristics and the stand age group; The sixth acquisition module is used to acquire carbon storage calibration coefficients based on the climate characteristics and the external influence characteristics. The seventh acquisition module is used to acquire forest carbon storage based on the vegetation carbon storage, the soil carbon storage, and the carbon storage calibration coefficient.

[0015] By adopting the above technical solution, target data of the target forest is first obtained, and forest stand characteristics, climate characteristics, and external influence characteristics are extracted from it. Then, based on the forest stand characteristics, forest type, stand density, stand age group, and soil characteristics are determined, and vegetation carbon storage and soil carbon storage are calculated separately. At the same time, carbon storage calibration coefficients are obtained by combining climate characteristics and external influence characteristics. Finally, forest carbon storage is obtained through vegetation carbon storage, soil carbon storage, and calibration coefficients. By using target data to comprehensively capture the multi-dimensional characteristics of forests, calculating vegetation and soil carbon storage separately and introducing calibration coefficients, it takes into account both the inherent carbon storage characteristics of forests and dynamic factors such as climate and external influences, effectively improving the accuracy and comprehensiveness of forest carbon storage calculation. It can better meet the needs of accurate estimation of forest carbon storage in large areas and provide a more reliable basis for addressing climate change and formulating forestry policies.

[0016] In summary, this application includes the following beneficial technical effects: By leveraging target data to comprehensively capture the multidimensional characteristics of forests, calculating vegetation and soil carbon storage separately and introducing calibration coefficients, this approach considers both the inherent carbon storage characteristics of forests and dynamic factors such as climate and external influences. This effectively improves the accuracy and comprehensiveness of forest carbon storage calculations, better meeting the needs for precise estimation of forest carbon storage in large areas and providing a more reliable basis for addressing climate change and formulating forestry policies. Attached Figure Description

[0017] Figure 1 This is a flowchart of the main process of a forest carbon storage calculation method according to an embodiment of this application; Figure 2 This is a flowchart of steps S201 to S203; Figure 3 This is a flowchart of steps S301 to S303; Figure 4 This is a flowchart of steps S401 to S406; Figure 5 This is a flowchart of steps S501 to S504; Figure 6 This is a flowchart of steps S601 to S606; Figure 7 This is a flowchart of steps S701 to S703; Figure 8 This is a flowchart of steps S801 to S802; Figure 9 This is a block diagram of a forest carbon storage calculation system according to an embodiment of this application.

[0018] Explanation of reference numerals in the attached figures: 1. First acquisition module; 2. Second acquisition module; 3. Third acquisition module; 4. Fourth acquisition module; 5. Fifth acquisition module; 6. Sixth acquisition module; 7. Seventh acquisition module. Detailed Implementation

[0019] Firstly, this application discloses a method for calculating forest carbon storage.

[0020] Reference Figure 1 A method for calculating forest carbon storage includes steps S101 to S107: Step S101: Obtain the target data corresponding to the target forest.

[0021] Specifically, the target data refers to data related to the target forest, including remote sensing data, field survey data, and video monitoring data.

[0022] Step S102: Based on the target data, obtain the stand characteristics, climate characteristics, and external influence characteristics corresponding to the target forest.

[0023] Specifically, in this embodiment, forest stand characteristics refer to indicators reflecting the attributes of the forest ecosystem, including forest type, stand density, stand age group, and soil characteristics; climate characteristics refer to the factors affecting the forest carbon cycle under regional climate conditions, including temperature characteristics, humidity characteristics, and disaster characteristics; and external influence characteristics refer to the interference of human activities or biological factors on the forest carbon cycle.

[0024] Step S103: Based on stand characteristics, obtain forest type, stand density, stand age group and soil characteristics.

[0025] Specifically, in this embodiment, forest type refers to the category classified according to climate characteristics and forest components (tree species), such as tropical rainforest coniferous forest, temperate deciduous broad-leaved forest, etc. Different forest types have different carbon storage capacities; stand density refers to the number of trees per unit area or canopy closure. The higher the stand density, the greater the carbon storage potential; stand age group refers to the time that the forest ecosystem has experienced from its formation to the present, usually divided by stand, and can be divided into stages such as young forest, middle-aged forest, near-mature forest, mature forest, and over-mature forest; soil characteristics refer to the inherent properties and state of the soil in the target forest, including soil type and soil fertility, etc.

[0026] Step S104: Obtain vegetation carbon storage based on forest type and stand density.

[0027] Specifically, in this embodiment, vegetation carbon storage refers to the amount of carbon fixed in forest vegetation (trunks, branches, leaves, and roots).

[0028] Step S105: Obtain soil carbon storage based on soil characteristics and stand age group.

[0029] Specifically, in this embodiment, soil carbon storage refers to the total amount of organic and inorganic carbon in the soil.

[0030] Step S106: Obtain carbon storage calibration coefficients based on climate characteristics and external influence characteristics.

[0031] Specifically, in this embodiment, the carbon storage calibration coefficient refers to the coefficient used to correct the comprehensive carbon storage estimate.

[0032] Step S107: Obtain forest carbon storage based on vegetation carbon storage, soil carbon storage, and carbon storage calibration coefficient.

[0033] Specifically, in this embodiment, forest carbon storage refers to the comprehensive carbon storage after calibration by the carbon storage calibration coefficient.

[0034] The forest carbon storage calculation method provided in this embodiment first acquires target data of the target forest, extracts stand characteristics, climate characteristics, and external influence characteristics from it, then determines the forest type, stand density, stand age group, and soil characteristics based on the stand characteristics, and then calculates the vegetation carbon storage and soil carbon storage separately. At the same time, it obtains carbon storage calibration coefficients by combining climate characteristics and external influence characteristics. Finally, it obtains the forest carbon storage by combining vegetation carbon storage, soil carbon storage, and calibration coefficients. By using target data to comprehensively capture the multi-dimensional characteristics of the forest, calculating vegetation and soil carbon storage separately and introducing calibration coefficients, it considers both the inherent carbon storage characteristics of the forest itself and dynamic factors such as climate and external influences, effectively improving the accuracy and comprehensiveness of forest carbon storage calculation. It can better meet the needs of accurate estimation of forest carbon storage in large areas and provide a more reliable basis for addressing climate change and formulating forestry policies.

[0035] Reference Figure 2 In one embodiment of this example, step S104, based on forest type and stand density, to obtain vegetation carbon storage includes steps S201 to S203: Step S201: Based on forest type, obtain vegetation information of different vegetation types within the sample area.

[0036] Specifically, the sample area refers to a representative area selected in the target forest for field measurement or remote sensing data analysis. In this embodiment, the target forest can be divided into several areas of equal size, and the area of ​​this area is the sample area. Vegetation information refers to a set of parameters reflecting vegetation characteristics, including vegetation level information, tree measurement factors, vegetation density, and vegetation coverage.

[0037] Step S202: Based on vegetation information, obtain vegetation layer information, tree measurement factors, vegetation density and vegetation coverage.

[0038] Specifically, in this embodiment, vegetation stratification information refers to the vertical structural layers in a forest ecosystem, defined by plant growth height, life form, and spatial location within the community. Based on vegetation stratification information, the target forest can be divided into tree layer, shrub layer, and herb layer. Tree measurement factors include vegetation diameter at breast height (DBH), plant height, and crown width. Vegetation density refers to the number of plants per unit area or the base area. Vegetation coverage rate refers to the proportion of the vertical projection area of ​​vegetation to the area of ​​the sample plot.

[0039] Step S203: Obtain vegetation carbon storage based on vegetation hierarchy information, tree measurement factors, vegetation density, and vegetation coverage.

[0040] Specifically, vegetation carbon storage = vegetation biomass × carbon content coefficient. In this embodiment, the carbon content coefficient is the proportion of carbon in the dry matter of vegetation. For most vegetation, it is taken as 0.45~0.5 (that is, 45%~50% of the biomass is carbon), and there are slight differences between different species. The calculation of vegetation biomass needs to be determined based on the vegetation layer information (tree layer, shrub layer and herb layer, etc.) and combined with its tree measurement factor, vegetation density and vegetation coverage and other parameters.

[0041] In this embodiment, tree biomass is typically estimated using parameters such as diameter at breast height (DBH) and tree height, combined with the species allometric growth equation. The formula for calculating the total tree biomass per unit area satisfies... Where B1 is the total biomass of trees per unit area, B 1i N represents the biomass of a single tree, N1 represents the vegetation density corresponding to the tree, and n represents the number of trees in the sample area.

[0042] It is worth noting that the method for calculating the biomass of a single tree meets the following requirements. Where a, b, and c are species-specific parameters of the tree, pre-set according to relevant standards or actual conditions, for example, a=0.00005, b=2.4, c=0.8, D1 is the diameter at breast height (DBH) of the tree, and H1 is the tree height; the calculation method for the number of trees within the sample area satisfies , of which S y The sample area is used; therefore, the vegetation carbon storage corresponding to trees is calculated as follows: Where C1 represents the vegetation carbon storage corresponding to trees, and C f1 denoted by , where is the carbon content coefficient corresponding to the trees, and S is the area of ​​the target forest.

[0043] Similarly, shrub biomass calculation needs to consider parameters such as ground diameter, plant height, and vegetation density. The total shrub biomass per unit area must satisfy the calculation formula. Where B2 is the total biomass of shrubs per unit area, B 2i N1 represents the biomass of a single shrub, N2 represents the vegetation density corresponding to the shrub, and m represents the number of shrubs within the sample area.

[0044] It is worth noting that the method for calculating the biomass of a single shrub meets the following requirements. Where k, j, l are species-specific parameters for shrubs, pre-set according to relevant standards or actual conditions, for example, k=0.002, m=1.5, n=0.6, D2 is the ground diameter of the shrub, and H2 is the height of the shrub; the calculation method for the number of shrubs within the sample area satisfies Therefore, the calculation method for vegetation carbon storage corresponding to shrubs is as follows: Where C2 represents the vegetation carbon storage corresponding to the shrubs, and C f2 This represents the carbon content coefficient corresponding to the shrub.

[0045] Herbaceous biomass is usually estimated using cover rate and biomass per unit area. The biomass per unit area of ​​herbaceous layer satisfies the calculation formula. Where B3 is the total herbaceous biomass per unit area, f3 is the proportion of herbaceous projected area to the sample area, and q3 is the herbaceous biomass coefficient per unit area; therefore, the vegetation carbon storage corresponding to herbaceous vegetation is calculated as follows: Where C3 represents the vegetation carbon storage corresponding to herbaceous plants, and C... f3 This represents the carbon content coefficient corresponding to the herbaceous plant.

[0046] Therefore, in this embodiment, the vegetation carbon storage C z =C1+C2+C3.

[0047] The forest carbon storage calculation method provided in this embodiment first determines the vegetation information of different vegetation types within the sample area based on forest type. Then, it extracts vegetation layer information, tree measurement factors, vegetation density, and vegetation coverage from the vegetation information. Finally, it calculates the vegetation carbon storage based on this information. By refining the specific parameters of the vegetation, it can more accurately reflect the contribution of different vegetation types to carbon storage, avoiding the errors caused by general calculations based solely on forest type. This further improves the accuracy of vegetation carbon storage calculation and lays a more solid foundation for the reliability of the overall forest carbon storage calculation results.

[0048] Reference Figure 3 In one embodiment of this example, step S105, based on soil characteristics and forest stand age group, obtains soil carbon storage, including steps S301 to S303: Step S301: Based on soil characteristics, obtain soil type and soil fertility.

[0049] Specifically, in this embodiment, soil type is a category classified according to soil formation process and texture (such as black soil, red soil, sandy soil, etc.), which determines the soil's water and fertilizer retention capacity and carbon sequestration potential (for example, black soil has a significantly higher carbon storage capacity than sandy soil due to its high organic matter content); soil fertility is an indicator for measuring the soil's ability to provide nutrients, mainly reflected by organic matter content, nitrogen, phosphorus and potassium concentrations, pH value, etc. (soil with high fertility can promote vegetation growth and indirectly increase the amount of carbon input by the root system).

[0050] Step S302: Obtain soil biological density based on soil fertility and soil forest age group.

[0051] Specifically, soil biomass density refers to the quantity or biomass density of various organisms (microorganisms, small animals, etc.) in the soil.

[0052] Step S303: Obtain soil carbon storage based on soil type, soil fertility, and soil biological density.

[0053] Specifically, in this embodiment, the formula for calculating soil carbon storage satisfies Wherein, T is the basic carbon pool coefficient of soil type. Different soil types have significantly different organic matter retention capacities. Soil types need to be classified based on characteristics such as soil texture, pH value, and clay content, and a basic carbon pool coefficient is assigned to each type. For example, black soil (high clay, low pH) has a T=1.2~1.5 (high carbon retention capacity); sandy soil (low clay, high permeability) has a T=0.6~0.8 (low carbon retention capacity); and red soil (acidic, highly weathered) has a T=0.8~1.0 (medium carbon retention capacity).

[0054] F is the organic matter input coefficient corresponding to soil fertility. Soil fertility can be quantified by indicators such as organic matter content, total nitrogen, and available phosphorus, and converted into the organic matter input coefficient. In this embodiment, for high fertility (organic matter content > 5%), F can be set to 1.0~1.2; for medium fertility (2% ≤ organic matter content ≤ 5%), F can be set to 0.7~0.9; and for low fertility (organic matter content < 2%), F can be set to 0.4~0.6.

[0055] B represents the soil biological transformation coefficient. In this embodiment, the soil biological transformation coefficient = soil biological density × age correction coefficient. The age correction coefficient varies depending on the age group of the forest stand. For example, the age correction coefficient for young forests is set to 0.6; the age correction coefficient for middle-aged forests is set to 0.8; and the age correction coefficient for mature forests is set to 1.0~1.2. In this embodiment, the age correction coefficient can also be set by the user according to the actual situation or user needs, combined with relevant standards.

[0056] E is the soil bulk density, which reflects the mass of soil per unit volume and can be obtained through prior actual measurement; R is the thickness of the sampled soil layer, which is set according to actual needs; P is the carbon loss rate, which is usually taken as 0.1~0.3 due to soil respiration, leaching, etc., with higher values ​​for sandy soil and lower values ​​for clay soil.

[0057] The forest carbon storage calculation method provided in this embodiment first determines the soil type and soil fertility based on soil characteristics, then combines the stand age group to obtain the soil biological density, and finally calculates the soil carbon storage based on the soil type, soil fertility, and soil biological density. By combining soil characteristics with stand age group, the key factors affecting soil carbon storage are comprehensively considered. In particular, the introduction of soil biological density as an intermediate variable more accurately reflects the carbon storage mechanism in the soil. Compared with traditional methods that only consider a single soil factor or ignore the influence of biological activity, this method can significantly improve the accuracy of soil carbon storage calculation, thereby enhancing the reliability and scientific validity of the overall forest carbon storage calculation results.

[0058] Reference Figure 4 In one embodiment of this example, step S106, based on climate characteristics and external influence characteristics, obtains the carbon storage calibration coefficient, including steps S401 to S406: Step S401: Based on vegetation information and climate characteristics, obtain the degree of climate matching.

[0059] Specifically, in this embodiment, the degree of climate matching refers to the adaptability of vegetation hierarchical information to local climate conditions, that is, the degree of fit between the physiological characteristics of vegetation (such as drought resistance and cold resistance) and climate characteristics. For example, tropical rainforests are well-matched with hot and humid climates, resulting in high photosynthetic efficiency and rapid carbon accumulation; while artificial coniferous forests are prone to pests and diseases in hot and humid areas, leading to a decline in carbon storage capacity.

[0060] Step S402: Based on climate characteristics, obtain temperature characteristics, humidity characteristics, and disaster characteristics.

[0061] Specifically, in this embodiment, temperature characteristics include regional average annual temperature, extreme temperatures, and seasonal temperature variations (e.g., average annual temperature of 25-28℃ in tropical forests, and significant seasonal temperature differences in temperate forests), which directly affect plant photosynthesis and microbial decomposition rates; humidity characteristics include annual precipitation, precipitation distribution, and air humidity (e.g., annual precipitation of over 2000mm in tropical rainforests, and less than 400mm in arid forests), which determine vegetation growth vitality and litter decomposition rate (humid environments accelerate decomposition, while dry environments slow down decomposition); disaster characteristics include climate-related natural disturbances (e.g., rainstorms, droughts, hurricanes, and periods of high incidence of pests and diseases), which may lead to vegetation death or abnormal litter loss, significantly reducing carbon storage in the short term.

[0062] Step S403: Obtain vegetation growth suitability based on temperature and humidity characteristics.

[0063] Specifically, in this embodiment, vegetation growth suitability refers to the degree of benefit of comprehensive temperature and humidity characteristics to vegetation growth, usually expressed by an index of 0-1 (1 being the most suitable). For example, when the temperature is in the optimal range for plant photosynthesis (20-25℃ for most plants) and the humidity meets the transpiration requirements, the suitability is high. In areas with high suitability, vegetation biomass grows rapidly, providing more litter and root residue to the soil.

[0064] Step S404: Obtain the litter generation rate based on climate matching degree and vegetation growth suitability.

[0065] Specifically, in this embodiment, litter includes fallen leaves, branches, bark, and dead vegetation, as well as other organic matter. The litter generation rate refers to the amount of litter generated per unit time. In areas with high climate compatibility and high growth suitability, vegetation grows vigorously and litter generation is fast. Litter is the main source of soil carbon, and its generation rate directly affects the efficiency of soil carbon accumulation.

[0066] Step S405: Based on the litter generation rate and the age group of the forest stand, obtain the litter cover thickness of the target forest.

[0067] Specifically, in this embodiment, the litter cover thickness refers to the vertical thickness of litter accumulated on the ground surface. As age increases, litter gradually accumulates (e.g., the cover thickness of mature forests can reach 5-10 cm, while that of young forests is usually less than 2 cm).

[0068] Step S406: Obtain carbon storage calibration coefficients based on disaster characteristics, litter cover thickness, and external impact characteristics.

[0069] The forest carbon storage calculation method provided in this embodiment first determines the climate matching degree based on vegetation information and climate characteristics. Simultaneously, it extracts temperature, humidity, and disaster characteristics from the climate characteristics. Then, it calculates vegetation growth suitability based on temperature and humidity characteristics. Next, it determines the litter generation rate through the climate matching degree and vegetation growth suitability, and obtains the litter cover thickness by combining forest stand age groups. Finally, it derives the carbon storage calibration coefficient by comprehensively considering disaster characteristics, litter cover thickness, and external influence characteristics. This process, by constructing a multi-dimensional dynamic correlation model between climate and litter, comprehensively captures the complex impact of environmental factors on the forest carbon cycle. In particular, by using litter cover thickness as a key intermediate variable, it organically combines climate conditions, vegetation growth, and external disturbances, enabling the calibration coefficient to more accurately reflect the actual dynamic changes in forest carbon storage. Compared to traditional methods that ignore the time-varying influence of environmental factors, this significantly improves the accuracy and adaptability of carbon storage calculation, providing more reliable support for the scientific assessment of forest carbon sink functions.

[0070] Reference Figure 5In one embodiment of this example, step S406, based on disaster characteristics, litter cover thickness, and external impact characteristics, obtains the carbon storage calibration coefficient, including steps S501 to S504: Step S501: Based on disaster characteristics, obtain the disaster type and the time when the disaster is likely to occur.

[0071] Specifically, in this embodiment, disaster type refers to the disaster category classified according to its cause and manifestation, mainly including climate-related disasters and biological disasters. Climate-related disasters include fires (caused by high temperature and drought), storms (strong winds causing trees to fall), floods (water accumulation causing root hypoxia), etc.; biological disasters include insect pests (such as pine wilt disease) and diseases (such as anthracnose), which affect carbon accumulation by damaging vegetation.

[0072] Step S502: Obtain the first calibration coefficient based on the disaster type and the time when the disaster is likely to occur.

[0073] Specifically, the first calibration coefficient refers to a coefficient calculated based on the disaster type and occurrence time, used to correct carbon reserves, reflecting the direct or potential loss of the carbon pool due to the disaster; in this embodiment, the calculation formula for the first calibration coefficient satisfies Where u represents the number of disaster types the target forest may encounter (such as fire, insect infestation, storm, etc.); α i Let β be the intensity coefficient of the i-th type of disaster. i γ represents the time-prone matching degree of the i-th type of disaster; i Let be the frequency coefficient of the i-th type of disaster.

[0074] In this embodiment, intensity coefficients (0-1) for different disasters can be defined first based on the disaster type. In this embodiment, the greater the degree of damage caused by the disaster, the greater the intensity coefficient corresponding to the disaster. Then, based on the combination of the disaster's susceptibility time with vegetation growth characteristics and growth cycle, a corresponding susceptibility time matching degree (0-1) is set to reflect the degree of overlap between the disaster's susceptibility time and the critical period of forest carbon accumulation. For example, if the disaster is prone to occur during the growing season (peak carbon accumulation period), the susceptibility time matching degree is set to 0.8-1.0; if it is prone to occur during the dormant period (slow carbon accumulation period), the matching degree is set to 0.8-1.0. Therefore, the time-proneness matching degree is set to 0.2-0.4; there is no obvious time correlation, so the time-proneness matching degree is set to 0.5; finally, the frequency coefficient of disaster occurrence (0-1) can also be obtained based on historical data. This coefficient is set based on the historical occurrence frequency. For high frequency (more than once a year), the frequency coefficient can be set to 0.8-1.0; for medium frequency (once every 3-5 years), the frequency coefficient can be set to 0.4-0.6; for low frequency (more than once every 10 years), the frequency coefficient can be set to 0.1-0.3.

[0075] Step S503: Obtain the second calibration coefficient based on the thickness of litter cover and external influence characteristics.

[0076] Specifically, in this embodiment, the second calibration coefficient refers to the coefficient calculated based on the thickness of litter cover and the characteristics of external influences, reflecting the impact of external disturbances on the litter-soil carbon conversion process. When the litter thickness is appropriate and the external disturbances are small, the coefficient is close to 1.

[0077] Step S504: Obtain the carbon storage calibration coefficient based on the first calibration coefficient and the second calibration coefficient.

[0078] Specifically, in this embodiment, the carbon storage calibration coefficient is a final correction coefficient obtained by combining the first calibration coefficient and the second calibration coefficient, which is used to adjust the basic carbon storage of vegetation and soil to make it closer to the actual value.

[0079] The forest carbon storage calculation method provided in this embodiment first determines the disaster type and disaster-prone time from disaster characteristics, and obtains a first calibration coefficient accordingly; then, it combines the thickness of litter cover and external impact characteristics to obtain a second calibration coefficient; finally, it calculates the carbon storage calibration coefficient using the first and second calibration coefficients. By combining the natural factor of disaster with litter cover thickness, external impact characteristics, etc., and obtaining calibration coefficients in a hierarchical manner, it can more accurately reflect the impact of different factors on carbon storage, thereby improving the accuracy of forest carbon storage calculation.

[0080] Reference Figure 6 In one embodiment of this example, step S503, based on the thickness of litter cover and external influence characteristics, obtains the second calibration coefficient, including steps S601 to S606: Step S601: Based on external influence characteristics, obtain target biological information and human information.

[0081] Specifically, in this embodiment, the target biological information refers to the biological species and their characteristics related to the dynamics of litter in the target forest, focusing on organisms that have a direct impact on litter, including the species (such as carrion insects and herbivores), quantity (density), and activity habits (such as the frequency of feeding on litter); human information refers to characteristic data related to human activities around the forest, focusing on human behavioral attributes that affect the dynamics of litter, including population size, activity frequency, lifestyle, etc., which are directly related to the degree of human intervention in litter.

[0082] Step S602: Based on the target biological information, obtain the target biological species and target biological density.

[0083] Specifically, in this embodiment, the target organism species refers to the category of organisms that specifically participate in or affect the litter process. According to their ecological roles, they can be divided into decomposers (such as fungi, bacteria, and earthworms, which accelerate the decomposition of litter into soil organic matter), consumers (such as locusts and wild rabbits, which directly feed on litter or fresh vegetation, reducing the source of litter), and disturbers (such as wild boars, which turn over the soil and destroy the litter cover). The target organism density refers to the number of individuals or biomass of the target organism per unit area. The higher the density, the greater the impact on litter.

[0084] Step S603: Based on the target species and target species density, obtain the first influence coefficient on the thickness of litter cover.

[0085] Specifically, in this embodiment, the first influence coefficient refers to the intensity of the effect of the target organism on the thickness of litter cover, reflecting the direct impact of biological activity on litter. In this embodiment, when the decomposer density is high, the coefficient may be greater than 1 (accelerating decomposition and reducing litter thickness); when the consumer density is high, the coefficient may be less than 1 (reducing litter retention). For example, if the earthworm density is extremely high, the first influence coefficient may be 1.2, indicating that the litter decomposition speed is increased by 20%.

[0086] Step S604: Based on human information, obtain population density, frequency of human activities, and lifestyle.

[0087] Specifically, in this embodiment, population density refers to the number of people per unit area, reflecting the potential intensity of human activities. In areas with high population density, human interference with forests (such as collecting litter for fuel) is more frequent, and the thickness of litter is more significantly affected. The frequency of human activities refers to the number of times humans enter the forest or carry out related activities per unit time, including logging, tourism, and farming. High-frequency activities may directly damage the litter cover layer (such as trampling and compaction) or indirectly affect its decomposition environment (such as soil compaction leading to decreased permeability). Lifestyle refers to the way humans rely on forest resources. According to the impact on litter, it can be divided into dependent and laissez-faire. Dependent refers to directly using litter (such as for fuel or fertilizer), resulting in its loss due to human activities. Laissez-faire refers to not directly obtaining litter, but activities (such as camping or road construction) may interfere with its cover.

[0088] Step S605: Obtain a second influence coefficient on the thickness of litter cover based on population density, frequency of human activities, and lifestyle.

[0089] Specifically, in this embodiment, the second influence coefficient quantifies the intensity of human activities on the thickness of litter cover, taking into account the effects of population density, activity frequency, and lifestyle. Under a dependent lifestyle, the higher the population density, the smaller the coefficient (the more litter is lost). Under a laissez-faire lifestyle, the higher the activity frequency, the greater the fluctuation of the coefficient (e.g., the coefficient may increase slightly when compaction slows down decomposition).

[0090] Step S606: Based on the thickness of the litter cover, the first influence coefficient, and the second influence coefficient, obtain the second calibration coefficient.

[0091] The forest carbon storage calculation method provided in this embodiment extracts target biological information and human information from external influence characteristics. The target biological information determines the target biological species and density and obtains a first influence coefficient on litter cover thickness. The human information obtains population density, frequency of human activities, and lifestyle and obtains a second influence coefficient. The second calibration coefficient is obtained by combining litter cover thickness, the first influence coefficient, and the second influence coefficient. By distinguishing the different influences of target biological and human factors, the method refines the path of action of external influence characteristics on litter cover thickness, making the calculation of the second calibration coefficient more in line with the actual situation. This provides support for the accurate acquisition of carbon storage calibration coefficients and improves the reliability of forest carbon storage calculation.

[0092] Reference Figure 7 In one embodiment of this example, step S606, based on the litter cover thickness, the first influence coefficient, and the second influence coefficient, obtains the second calibration coefficient, which includes steps S701 to S703: Step S701: If the lifestyle is a dependent lifestyle, then obtain the demand for litter based on population density.

[0093] Specifically, in this embodiment, the demand for litter refers to the total amount of litter obtained by humans per unit time to meet their living needs, which is determined by both population density and demand per unit population.

[0094] Step S702: Based on the demand for fallen debris, obtain the demand influence coefficient on the thickness of fallen debris, and use the demand influence coefficient as the second influence coefficient.

[0095] Specifically, in this embodiment, the demand impact coefficient refers to the degree of human influence on litter thickness under a quantitatively dependent lifestyle, and is used as a parameter to correct the second impact coefficient. Its calculation formula is as follows: Where K1 is the demand impact coefficient, W is the population density, A is the annual litter demand per unit population, and Y is the annual natural litter generation in the target area. is the litter regeneration compensation coefficient, and μ is the regional adjustment coefficient.

[0096] Step S703: If the lifestyle is a laissez-faire lifestyle, then obtain the activity impact coefficient on the thickness of litter based on population density and activity frequency, and use the activity impact coefficient as the second impact coefficient.

[0097] Specifically, in this embodiment, the formula for calculating the activity impact coefficient is as follows: Where K2 is the activity impact coefficient, Here, V represents the weighting coefficient, and V represents the activity frequency. This is the activity intensity coefficient. is the activity type attenuation coefficient, and Y is the anti-interference threshold for litter.

[0098] The forest carbon storage calculation method provided in this embodiment determines the demand for litter based on population density if the lifestyle is dependent, and then calculates the demand impact coefficient on litter thickness using the demand impact coefficient calculation formula, which is used as the second impact coefficient. If the lifestyle is laissez-faire, the activity impact coefficient on litter thickness is obtained based on population density and activity frequency using the activity impact coefficient calculation formula, which is used as the second impact coefficient. Differentiated calculation methods are used for the impact of different lifestyles on litter thickness, and the degree of impact is quantified through specific formulas, making the determination of the second impact coefficient more scientific and accurate, further improving the reliability of the carbon storage calibration coefficient, thereby helping to improve the accuracy of forest carbon storage calculation.

[0099] Reference Figure 8 In one embodiment of this example, step S107, which obtains forest carbon storage based on vegetation carbon storage, soil carbon storage, and carbon storage calibration coefficient, includes steps S801 to S802: Step S801: Obtain the comprehensive carbon storage based on vegetation carbon storage and soil carbon storage.

[0100] Specifically, in this embodiment, the total carbon storage refers to the sum of vegetation carbon storage and soil carbon storage, which is the basic total amount of forest carbon pool and reflects the overall scale of carbon sequestration in forest ecosystems.

[0101] Step S802: Obtain forest carbon storage based on comprehensive carbon storage and carbon storage calibration coefficient.

[0102] The forest carbon storage calculation method provided in this embodiment first adds vegetation carbon storage and soil carbon storage to obtain a comprehensive carbon storage, and then multiplies this comprehensive carbon storage by a carbon storage calibration coefficient to obtain the forest carbon storage. By first integrating the two core components of forest carbon storage, vegetation and soil, to obtain the basic total amount, and then correcting it with the calibration coefficient, this method not only comprehensively considers the main carbon storage carriers in the forest ecosystem, but also makes up for possible deviations in the basic calculation through the calibration mechanism, so that the final forest carbon storage is more in line with the actual situation and effectively improves the accuracy of the calculation results.

[0103] Secondly, this application also discloses a forest carbon storage calculation system.

[0104] Reference Figure 9A forest carbon storage calculation system, comprising: The first acquisition module is used to acquire the target data corresponding to the target forest; The second acquisition module is used to acquire the stand characteristics, climate characteristics, and external influence characteristics of the target forest based on the target data. The third acquisition module is used to acquire forest type, stand density, stand age group and soil characteristics based on stand characteristics; The fourth acquisition module is used to acquire vegetation carbon storage based on forest type and stand density; The fifth acquisition module is used to acquire soil carbon storage based on soil characteristics and stand age group; The sixth acquisition module is used to obtain carbon storage calibration coefficients based on climate characteristics and external influence characteristics; The seventh acquisition module is used to acquire forest carbon storage based on vegetation carbon storage, soil carbon storage, and carbon storage calibration coefficient.

[0105] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A method for calculating forest carbon storage, characterized in that, include: Obtain the target data corresponding to the target forest; Based on the target data, obtain the stand characteristics, climate characteristics, and external influence characteristics corresponding to the target forest; Based on the aforementioned stand characteristics, forest type, stand density, stand age group, and soil properties are obtained. Based on the forest type and the stand density, the vegetation carbon storage is obtained; Soil carbon storage was obtained based on the soil characteristics and the stand age group. Based on the aforementioned climate characteristics and external influence characteristics, a carbon storage calibration coefficient is obtained; Forest carbon storage is obtained based on the vegetation carbon storage, the soil carbon storage, and the carbon storage calibration coefficient.

2. The method for calculating forest carbon storage according to claim 1, characterized in that, The process of obtaining vegetation carbon storage based on the forest type and the stand density includes: Based on the forest type, vegetation information of different vegetation types within the sample area is obtained; Based on the vegetation information, vegetation hierarchical information, tree measurement factors, vegetation density, and vegetation coverage are obtained. The vegetation carbon storage is obtained based on the vegetation hierarchy information, the tree measurement factor, the vegetation density, and the vegetation coverage.

3. The method for calculating forest carbon storage according to claim 1, characterized in that, The process of obtaining soil carbon storage based on the soil characteristics and the stand age group includes: Based on the aforementioned soil characteristics, the soil type and soil fertility are determined; Based on the soil fertility and the soil forest age group, the soil biological density was obtained; Soil carbon storage is obtained based on the soil type, soil fertility, and soil biological density.

4. The method for calculating forest carbon storage according to claim 1, characterized in that, The process of obtaining the carbon storage calibration coefficient based on the climate characteristics and the external influence characteristics includes: Based on vegetation information and the aforementioned climate characteristics, the degree of climate matching is obtained; Based on the aforementioned climate characteristics, temperature characteristics, humidity characteristics, and disaster characteristics are obtained; Based on the temperature and humidity characteristics, the suitability for vegetation growth is obtained; Based on the climate matching degree and the vegetation growth suitability, the litter generation rate is obtained; Based on the litter generation rate and the stand age group, the litter cover thickness of the target forest is obtained; Based on the disaster characteristics, the thickness of the litter cover, and the external impact characteristics, a carbon storage calibration coefficient is obtained.

5. The method for calculating forest carbon storage according to claim 4, characterized in that, The process of obtaining the carbon storage calibration coefficient based on the disaster characteristics, the thickness of the litter cover, and the external impact characteristics includes: Based on the aforementioned disaster characteristics, the disaster type and the time when the disaster is likely to occur are obtained; Based on the disaster type and the disaster's occurrence time, a first calibration coefficient is obtained; Based on the thickness of the fallen debris cover and the external influence characteristics, a second calibration coefficient is obtained; The carbon storage calibration coefficient is obtained based on the first calibration coefficient and the second calibration coefficient.

6. The method for calculating forest carbon storage according to claim 5, characterized in that, The process of obtaining the second calibration coefficient based on the litter cover thickness and the external influence characteristics includes: Based on the aforementioned external influence characteristics, target biological information and human information are obtained; Based on the target biological information, the target biological species and target biological density are obtained; Based on the target organism species and the target organism density, a first influence coefficient on the thickness of the litter cover is obtained; Based on the aforementioned human information, population density, frequency of human activities, and lifestyles are obtained; Based on the population density, the frequency of human activities, and the lifestyle, a second influence coefficient on the thickness of the litter cover is obtained; A second calibration coefficient is obtained based on the thickness of the fallen debris coverage, the first influence coefficient, and the second influence coefficient.

7. The method for calculating forest carbon storage according to claim 6, characterized in that, The process of obtaining the second influence coefficient on the thickness of the litter based on the population density, the frequency of human activities, and the lifestyle includes: If the lifestyle described is a dependent lifestyle, then the demand for litter is obtained based on the population density. Based on the demand for fallen debris, an impact coefficient on the thickness of the fallen debris is obtained, and this impact coefficient is used as the second impact coefficient. The formula for calculating the impact coefficient is as follows: Where K1 is the demand impact coefficient, P is the population density, Q is the annual litter demand per unit population, and S is the annual natural litter generation in the target area. μ is the litter regeneration compensation coefficient, and μ is the regional adjustment coefficient. If the lifestyle is a laissez-faire lifestyle, then the activity impact coefficient on the thickness of the litter is obtained based on the population density and the activity frequency, and this activity impact coefficient is used as the second impact coefficient. The calculation formula for the activity impact coefficient is as follows: Where K2 is the activity impact coefficient, Here, F represents the weighting coefficient, and F represents the activity frequency. This is the activity intensity coefficient. is the activity type attenuation coefficient, and T is the anti-interference threshold for litter.

8. The method for calculating forest carbon storage according to claim 1, characterized in that, The process of obtaining forest carbon storage based on the vegetation carbon storage, the soil carbon storage, and the carbon storage calibration coefficient includes: Based on the vegetation carbon storage and the soil carbon storage, obtain the comprehensive carbon storage; Forest carbon storage is obtained based on the comprehensive carbon storage and the carbon storage calibration coefficient.

9. A forest carbon storage calculation system, characterized in that, include: The first acquisition module is used to acquire the target data corresponding to the target forest; The second acquisition module is used to acquire the stand characteristics, climate characteristics and external influence characteristics of the target forest based on the target data. The third acquisition module is used to acquire forest type, stand density, stand age group and soil characteristics based on the stand characteristics. The fourth acquisition module is used to acquire vegetation carbon storage based on the forest type and the stand density; The fifth acquisition module is used to acquire soil carbon storage based on the soil characteristics and the stand age group; The sixth acquisition module is used to acquire carbon storage calibration coefficients based on the climate characteristics and the external influence characteristics. The seventh acquisition module is used to acquire forest carbon storage based on the vegetation carbon storage, the soil carbon storage, and the carbon storage calibration coefficient.

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