A forest carbon stock calculation system and method

Abstract

The application relates to the technical field of forest resource monitoring, in particular to a forest carbon storage calculation system and method, which comprises the following steps: obtaining target data corresponding to a target forest; obtaining stand characteristics, climate characteristics and external influence characteristics corresponding to the target forest based on the target data; obtaining forest types, stand densities, stand age groups and soil characteristics based on the stand characteristics; obtaining vegetation carbon storage based on the forest types and the stand densities; obtaining soil carbon storage based on the soil characteristics and the stand age groups; obtaining a carbon storage calibration coefficient based on the climate characteristics and the external influence characteristics; and obtaining forest carbon storage based on the vegetation carbon storage, the soil carbon storage and the carbon storage calibration coefficient. The application helps to improve the calculation accuracy of carbon storage.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of forest resource monitoring, and in particular to a forest carbon storage calculation system and method. BACKGROUND

[0002] Forest carbon storage is an important indicator of forest ecosystem carbon sink function. Accurate calculation of forest carbon storage is of great significance for addressing climate change and developing forestry policies. Traditional forest carbon storage calculation methods are mainly based on plot survey data, and the carbon storage is estimated by establishing a regression model of biomass and tree height, diameter at breast height, etc. However, this method has the problems of single data, insufficient spatial representation, low calculation efficiency, etc., and it is difficult to meet the demand of accurate estimation of forest carbon storage in large areas.

[0003] With the development of remote sensing technology, remote sensing image data is widely used in forest carbon storage calculation. However, most of the 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 combining multiple data sources is needed to improve the calculation accuracy. SUMMARY

[0004] In order to help improve the calculation accuracy of carbon storage, the present application provides a forest carbon storage calculation system and method.

[0005] In the first aspect, the present application provides a forest carbon storage calculation method, which adopts the following technical scheme:

[0006] A forest carbon storage calculation method comprises:

[0007] Obtaining target data corresponding to a target forest;

[0008] Based on the target data, obtaining forest stand characteristics, climate characteristics and external influence characteristics corresponding to the target forest;

[0009] Based on the forest stand characteristics, obtaining forest type, stand density, stand age group and soil characteristics;

[0010] Based on the forest type and the stand density, obtaining vegetation carbon storage;

[0011] Based on the soil characteristics and the stand age group, obtaining soil carbon storage;

[0012] Based on the climate characteristics and the external influence characteristics, obtaining a carbon storage calibration coefficient;

[0013] Based on the vegetation carbon storage, the soil carbon storage and the carbon storage calibration coefficient, obtaining forest carbon storage.

[0014] By adopting the technical scheme, target data of a target forest is first acquired, from which forest stand characteristics, climate characteristics and external influence characteristics are extracted, then the forest type, stand density, stand age group and soil characteristics are determined according to the forest stand characteristics, and then the vegetation carbon storage and the soil carbon storage are calculated respectively, meanwhile, the carbon storage calibration coefficient is obtained by combining the climate characteristics and the external influence characteristics, and finally the forest carbon storage is obtained by the vegetation carbon storage, the soil carbon storage and the calibration coefficient; the target data is used to realize comprehensive capture of multi-dimensional characteristics of the forest, the vegetation carbon storage and the soil carbon storage are calculated separately and the calibration coefficient is introduced, which not only considers inherent carbon storage characteristics of the forest, but also takes into account dynamic factors such as climate and external influence, thereby effectively improving the accuracy and comprehensiveness of the forest carbon storage calculation, and better meeting the demand for accurate estimation of forest carbon storage in a large area, and providing a more reliable basis for responding to climate change and formulating forestry policies.

[0015] Optionally, the obtaining of the vegetation carbon storage based on the forest type and the stand density comprises:

[0016] obtaining vegetation information of different vegetation in a sample area based on the forest type;

[0017] obtaining vegetation hierarchical information, tree measurement factors, vegetation density and vegetation coverage based on the vegetation information;

[0018] obtaining the vegetation carbon storage based on the vegetation hierarchical information, the tree measurement factors, the vegetation density and the vegetation coverage.

[0019] Optionally, the obtaining of the soil carbon storage based on the soil characteristics and the stand age group comprises:

[0020] obtaining soil types and soil fertility based on the soil characteristics;

[0021] obtaining soil biological density based on the soil fertility and the soil stand age group;

[0022] obtaining the soil carbon storage based on the soil types, the soil fertility and the soil biological density.

[0023] Optionally, the obtaining of the carbon storage calibration coefficient based on the climate characteristics and the external influence characteristics comprises:

[0024] obtaining a climate matching degree based on the vegetation information and the climate characteristics;

[0025] obtaining temperature characteristics, humidity characteristics and disaster characteristics based on the climate characteristics;

[0026] obtaining vegetation growth suitability based on the temperature characteristics and the humidity characteristics;

[0027] obtaining a litter production rate based on the climate matching degree and the vegetation growth suitability;

[0028] obtaining a litter cover thickness of the target forest based on the litter production rate and the stand age class;

[0029] obtaining a carbon storage calibration coefficient based on the disaster feature, the litter cover thickness and the external influence feature.

[0030] Optionally, the obtaining of the carbon storage calibration coefficient based on the disaster feature, the litter cover thickness and the external influence feature comprises:

[0031] obtaining a disaster type and a disaster prone time based on the disaster feature;

[0032] obtaining a first calibration coefficient based on the disaster type and the disaster prone time;

[0033] obtaining a second calibration coefficient based on the litter cover thickness and the external influence feature;

[0034] obtaining the carbon storage calibration coefficient based on the first calibration coefficient and the second calibration coefficient.

[0035] Optionally, the obtaining of the second calibration coefficient based on the litter cover thickness and the external influence feature comprises:

[0036] obtaining target biological information and human information based on the external influence feature;

[0037] obtaining a target biological species and a target biological density based on the target biological information;

[0038] obtaining a first influence coefficient on the litter cover thickness based on the target biological species and the target biological density;

[0039] obtaining a population density, a human activity frequency and a lifestyle based on the human information;

[0040] obtaining a second influence coefficient on the litter cover thickness based on the population density, the human activity frequency and the lifestyle;

[0041] obtaining the second calibration coefficient based on the litter cover thickness, the first influence coefficient and the second influence coefficient.

[0042] Optionally, the obtaining of the second influence coefficient on the litter thickness based on the population density, the human activity frequency and the lifestyle comprises:

[0043] if the lifestyle is a dependent lifestyle, obtaining a litter demand amount based on the population density;

[0044] based on the litter demand amount, obtaining a demand influence coefficient of the litter thickness, and taking the demand influence coefficient as the second influence coefficient, the calculation formula of the demand influence coefficient being:

[0045]

[0046] wherein K1 is the demand influence coefficient, P is the population density, Q is the annual litter demand amount per capita, S is the annual natural litter generation amount of the target region, is a litter regeneration compensation coefficient, and μ is a regional adjustment coefficient;

[0047] if the lifestyle is a permissive lifestyle, obtaining an activity influence coefficient of the litter thickness based on the population density and the activity frequency, and taking the activity influence coefficient as the second influence coefficient, the calculation formula of the activity influence coefficient being:

[0048]

[0049] wherein K2 is the activity influence coefficient, is a weight coefficient, F is the activity frequency, is an activity intensity coefficient, is an activity type attenuation coefficient, and T is a litter anti-interference threshold.

[0050] Optionally, the obtaining of the forest carbon storage based on the vegetation carbon storage, the soil carbon storage and the carbon storage calibration coefficient comprises:

[0051] based on the vegetation carbon storage and the soil carbon storage, obtaining a comprehensive carbon storage;

[0052] based on the comprehensive carbon storage and the carbon storage calibration coefficient, obtaining a forest carbon storage.

[0053] In a second aspect, the present application further discloses a forest carbon storage calculation system, which adopts the following technical scheme:

[0054] The forest carbon storage calculation system comprises:

[0055] a first obtaining module, configured to obtain target data corresponding to a target forest;

[0056] a second obtaining module, configured to obtain stand characteristics, climate characteristics and external influence characteristics corresponding to the target forest based on the target data;

[0057] The third obtaining module is configured to obtain a forest type, stand density, stand age group and soil property based on the stand feature;

[0058] The fourth obtaining module is configured to obtain a vegetation carbon storage based on the forest type and the stand density;

[0059] The fifth obtaining module is configured to obtain a soil carbon storage based on the soil property and the stand age group;

[0060] The sixth obtaining module is configured to obtain a carbon storage calibration coefficient based on the climate feature and the external influence feature;

[0061] The seventh obtaining module is configured to obtain a forest carbon storage based on the vegetation carbon storage, the soil carbon storage and the carbon storage calibration coefficient.

[0062] By adopting the above technical solution, the target data of the target forest is obtained first, the stand feature, the climate feature and the external influence feature are extracted therefrom, the forest type, the stand density, the stand age group and the soil property are determined according to the stand feature, the vegetation carbon storage and the soil carbon storage are calculated respectively, the carbon storage calibration coefficient is obtained by combining the climate feature and the external influence feature, and finally the forest carbon storage is obtained by the vegetation carbon storage, the soil carbon storage and the calibration coefficient; the multi-dimensional features of the forest are comprehensively captured by means of the target data, the vegetation carbon storage and the soil carbon storage are calculated separately and the calibration coefficient is introduced, the inherent carbon storage characteristics of the forest are considered, and the dynamic factors such as the climate and the external influence are also taken into account, the accuracy and the comprehensiveness of the forest carbon storage calculation are effectively improved, the demand for accurate estimation of the forest carbon storage in a large area is better met, and more reliable basis is provided for coping with climate change, formulating forestry policies and the like.

[0063] In summary, the present application has the following beneficial technical effects:

[0064] The multi-dimensional features of the forest are comprehensively captured by means of the target data, the vegetation carbon storage and the soil carbon storage are calculated separately and the calibration coefficient is introduced, the inherent carbon storage characteristics of the forest are considered, and the dynamic factors such as the climate and the external influence are also taken into account, the accuracy and the comprehensiveness of the forest carbon storage calculation are effectively improved, the demand for accurate estimation of the forest carbon storage in a large area is better met, and more reliable basis is provided for coping with climate change, formulating forestry policies and the like. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 is the main flowchart of a forest carbon storage calculation method according to an embodiment of the present application;

[0066] Figure 2 is the step flowchart of steps S201 to S203;

[0067] Figure 3 is a step flowchart of steps S301 to S303;

[0068] Figure 4 is a step flowchart of steps S401 to S406;

[0069] Figure 5 is a step flowchart of steps S501 to S504;

[0070] Figure 6 is a step flowchart of steps S601 to S606;

[0071] Figure 7 is a step flowchart of steps S701 to S703;

[0072] Figure 8 is a step flowchart of steps S801 to S802;

[0073] Figure 9 is a module diagram of a forest carbon storage calculation system according to an embodiment of the present application.

[0074] Legend of reference signs:

[0075] 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 DESCRIPTION

[0076] In a first aspect, the present application discloses a forest carbon storage calculation method.

[0077] With reference to Figure 1 A forest carbon storage calculation method includes steps S101 to S107:

[0078] Step S101: Obtain target data corresponding to a target forest.

[0079] Specifically, the target data is data related to the target forest, including remote sensing data, on-site survey data, and video monitoring data, etc.

[0080] Step S102: Based on the target data, obtain stand characteristics, climate characteristics, and external influence characteristics corresponding to the target forest.

[0081] Specifically, in this embodiment, the stand characteristics refer to indexes reflecting the properties of the forest ecosystem, including forest type, stand density, stand age group, and soil characteristics, etc.; the climate characteristics refer to the influence factors of regional climate conditions on forest carbon cycle, including temperature characteristics, humidity characteristics, and disaster characteristics, etc.; the external influence characteristics refer to the interference of human activities or biological factors on forest carbon cycle.

[0082] Step S103: Obtain forest type, stand density, stand age group and soil property based on stand characteristics.

[0083] Specifically, in this embodiment, the forest type refers to a category divided according to climate characteristics and composition of the forest (tree species), such as tropical rainforest coniferous forest, temperate deciduous broad-leaved forest, etc., and different forest types have different carbon storage capacities; the stand density refers to the number of trees per unit area or canopy density, and the higher the stand density, the greater the carbon storage potential; the stand age group refers to the time experienced by the forest ecosystem from formation to the present, which is 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; the soil property refers to the inherent properties and state of the soil in the target forest, including soil type and soil fertility, etc.

[0084] Step S104: Obtain vegetation carbon storage based on the forest type and the stand density.

[0085] Specifically, in this embodiment, the vegetation carbon storage refers to the amount of carbon fixed in the forest vegetation (trunk, branches and leaves, root system).

[0086] Step S105: Obtain soil carbon storage based on the soil property and the stand age group.

[0087] Specifically, in this embodiment, the soil carbon storage refers to the total amount of organic carbon and inorganic carbon in the soil.

[0088] Step S106: Obtain carbon storage calibration coefficient based on climate characteristics and external influence characteristics.

[0089] Specifically, in this embodiment, the carbon storage calibration coefficient refers to a coefficient for correcting the estimated comprehensive carbon storage.

[0090] Step S107: Obtain forest carbon storage based on the vegetation carbon storage, the soil carbon storage and the carbon storage calibration coefficient.

[0091] Specifically, in this embodiment, the forest carbon storage refers to the comprehensive carbon storage after being calibrated by the carbon storage calibration coefficient.

[0092] The forest carbon storage calculation method provided by the embodiment first acquires target data of a target forest, extracts stand characteristics, climate characteristics and external influence characteristics therefrom, then determines a forest type, a stand density, a stand age group and soil properties according to the stand characteristics, further calculates a vegetation carbon storage and a soil carbon storage respectively, obtains a carbon storage calibration coefficient by combining the climate characteristics and the external influence characteristics, and finally obtains the forest carbon storage by means of the vegetation carbon storage, the soil carbon storage and the calibration coefficient. With the aid of the target data, multi-dimensional characteristics of the forest are comprehensively captured, the vegetation carbon storage and the soil carbon storage are calculated separately and the calibration coefficient is introduced, the inherent carbon storage characteristics of the forest are considered, dynamic factors such as climate and external influences are also taken into account, the accuracy and comprehensiveness of the forest carbon storage calculation are effectively improved, and the demand for accurate estimation of forest carbon storage in a large area can be better met, thereby providing a more reliable basis for responding to climate change and formulating forestry policies.

[0093] With reference to Figure 2 In one of the implementation manners of the embodiment, the step S104 acquires the vegetation carbon storage based on the forest type and the stand density, including steps S201 to S203.

[0094] The step S201 acquires vegetation information of different vegetation in a sample area based on the forest type.

[0095] Specifically, the sample area refers to a representative region selected in the target forest for field measurement or remote sensing data analysis. In the embodiment, the target forest can be divided into a plurality of regions with equal areas, and the area of the region is the sample area. The vegetation information refers to a parameter set reflecting the characteristics of the vegetation, including vegetation hierarchical information, tree measurement factors, vegetation density and vegetation coverage.

[0096] The step S202 acquires the vegetation hierarchical information, the tree measurement factors, the vegetation density and the vegetation coverage based on the vegetation information.

[0097] Specifically, in the embodiment, the vegetation hierarchical information refers to the vertical structure level in the forest ecosystem, which is divided according to the plant growth height, the life form and the spatial position in the community. According to the vegetation hierarchical information, the target forest can be divided into a tree layer, a shrub layer and a herb layer. The tree measurement factors include vegetation diameter at breast height, tree height and crown width, etc. The vegetation density refers to the number of plants or basal area per unit area. The vegetation coverage refers to the proportion of the vertical projection area of the vegetation to the sample area.

[0098] The step S203 acquires the vegetation carbon storage based on the vegetation hierarchical information, the tree measurement factors, the vegetation density and the vegetation coverage.

[0099] Specifically, the 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 the vegetation. Most vegetation takes 0.45-0.5 (i.e., 45%-50% of the biomass is carbon). There are slight differences between different species. The calculation of the vegetation biomass needs to be determined according to the vegetation layer information (tree layer, shrub layer, herb layer, etc.), combined with the tree measuring factors, vegetation density and vegetation coverage and other parameters.

[0100] In this embodiment, the tree biomass is usually estimated by the diameter at breast height and the height and other parameters, combined with the allometric equation of the species. The total tree biomass per unit area satisfies the calculation formula , wherein B1 is the total tree biomass per unit area, B 1i is the biomass of a single tree, N1 is the vegetation density corresponding to the tree, and n is the number of tree trunks in the sample area.

[0101] It is worth noting that the calculation of the biomass of a single tree satisfies , wherein a, b, and c are specific parameters of the tree species, which are preset according to the relevant standards or actual conditions, for example, a = 0.00005, b = 2.4, and c = 0.8. D1 is the diameter at breast height of the tree, and H1 is the height of the tree. The calculation of the number of tree trunks in the sample area satisfies , wherein S y is the sample area; therefore, the calculation of the vegetation carbon storage corresponding to the tree satisfies , wherein C1 is the vegetation carbon storage corresponding to the tree, C f1 is the carbon content coefficient corresponding to the tree, and S is the area of the target forest.

[0102] Similarly, the calculation of the shrub biomass needs to be combined with the ground diameter, height and vegetation density and other parameters. The total shrub biomass per unit area satisfies the calculation formula , wherein B2 is the total shrub biomass per unit area, B 2i is the biomass of a single shrub, N2 is the vegetation density corresponding to the shrub, and m is the number of shrub trunks in the sample area.

[0103] It is worth noting that the calculation of the biomass of a single shrub satisfies , wherein k, j, and l are specific parameters of the shrub species, which are preset according to the relevant standards or actual conditions, for example, k = 0.002, m = 1.5, and n = 0.6. D2 is the ground diameter of the shrub, and H2 is the height of the shrub. The calculation of the number of shrub trunks in the sample area satisfies ; therefore, the calculation of the vegetation carbon storage corresponding to the shrub satisfies , wherein C2 is the vegetation carbon storage corresponding to the shrub, C f2 is the carbon content coefficient corresponding to the shrub.

[0104] The herb biomass is usually estimated by coverage and biomass coefficient per unit area, and the herb biomass per unit area satisfies the calculation formula wherein, B3 is the total herb biomass per unit area, f3 is the proportion of herb projection area to sample area, and q3 is the herb biomass coefficient per unit area; therefore, the calculation method of the vegetation carbon storage corresponding to the herb is wherein, C3 is the vegetation carbon storage corresponding to the herb, C f3 is the carbon content coefficient corresponding to the herb.

[0105] Therefore, in the embodiment, the vegetation carbon storage C z =C1+C2+C3.

[0106] The forest carbon storage calculation method provided by the embodiment determines the vegetation information of different vegetation in the sample area based on the forest type, extracts vegetation hierarchical information, tree measurement factors, vegetation density and vegetation coverage from the vegetation information, and finally calculates the vegetation carbon storage according to the information; by refining the specific parameters of the vegetation, the contribution of different vegetation in carbon storage can be more accurately reflected, the error caused by general calculation according to the forest type is avoided, and the accuracy of the vegetation carbon storage calculation is further improved, thereby laying a more solid foundation for the reliability of the entire forest carbon storage calculation result.

[0107] Referring to Figure 3 In one of the embodiments of the embodiment, the step S105 obtains the soil carbon storage based on the soil properties and stand age group, including steps S301 to S303:

[0108] Step S301: Obtain the soil type and soil fertility based on the soil properties.

[0109] Specifically, in the embodiment, the soil type is a category divided according to soil forming process and texture (such as black soil, red soil, sandy soil, etc.), which determines the water and fertilizer retention capacity and carbon sequestration potential of the soil (for example, the carbon storage of black soil is significantly higher than that of sandy soil due to high organic matter content); the soil fertility is an index for measuring the ability of soil to provide nutrients, which is mainly reflected by organic matter content, nitrogen, phosphorus and potassium concentration, pH value, etc. (soil with high fertility can promote the growth of vegetation, and indirectly increase the amount of carbon input by roots).

[0110] Step S302: Obtain the soil biological density based on the soil fertility and soil stand age group.

[0111] Specifically, the soil biological density refers to the number or biomass density of various organisms (microorganisms, small animals, etc.) in the soil.

[0112] Step S303: Obtain the soil carbon storage based on the soil type, soil fertility and soil biological density.

[0113] Specifically, in the present embodiment, the calculation formula of soil carbon storage satisfies wherein T is the base carbon pool coefficient of soil type, the organic matter retention ability of different soil types is significantly different, and the type needs to be divided according to the characteristics such as soil texture, pH value, and clay content, and the base carbon pool coefficient is given, for example, black soil (high clay content, low pH), T = 1.2~1.5 (high carbon retention ability); sandy soil (low clay content, high air permeability) T = 0.6~0.8 (low carbon retention ability); red soil (acidic, high weathering), T = 0.8~1.0 (moderate carbon retention ability).

[0114] F is the organic matter input coefficient corresponding to the soil fertility, the soil fertility can be quantified by indexes such as organic matter content, total nitrogen, and available phosphorus, and converted into the organic matter input coefficient; in the present embodiment, high fertility (organic matter content > 5%), then F = 1.0~1.2 can be set, medium fertility (2%≤organic matter content≤5%), then F = 0.7~0.9 can be set; low fertility (organic matter content < 2%), then F = 0.4~0.6 can be set.

[0115] B is the soil biological conversion coefficient, in the present embodiment, the soil biological conversion coefficient = soil biological density × age correction coefficient, the age correction coefficient is different according to different stand age groups, for example, the age correction coefficient of young forest is set to 0.6; the age correction coefficient of middle-aged forest is set to 0.8; the age correction coefficient of mature forest is set to 1.0~1.2; in the present embodiment, the age correction coefficient can also be set by the user according to the actual situation or user demand, combined with relevant standards.

[0116] E is the soil bulk density, reflecting the mass of unit volume of soil, which can be obtained by pre-measurement; R is the sampling soil layer thickness, 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., and the sandy soil is higher and the clay soil is lower.

[0117] The forest carbon storage calculation method provided by the present embodiment first determines the soil type and soil fertility based on the soil characteristics, then combines the stand age group to obtain the soil biological density, and finally calculates the soil carbon storage according to the soil type, soil fertility, and soil biological density; By combining soil characteristics with stand age groups, the key factors affecting soil carbon storage are comprehensively considered, especially by introducing the intermediate variable of soil biological density, which more accurately reflects the storage mechanism of carbon in soil. Compared with the traditional method which only considers a single soil factor or ignores the influence of biological activity, the accuracy of soil carbon storage calculation can be significantly improved, thereby improving the reliability and scientificity of the whole forest carbon storage calculation result.

[0118] Reference Figure 4In one of the implementation manners of the embodiment, the step S106 includes steps S401-S406 for obtaining the carbon storage calibration coefficient based on the climate feature and the external influence feature.

[0119] The step S401 is to obtain a climate matching degree based on the vegetation information and the climate feature.

[0120] Specifically, in the embodiment, the climate matching degree refers to the adaptability of the vegetation level information to the local climate condition, i.e., the matching degree of the physiological characteristics (such as drought resistance and cold resistance) of the vegetation to the climate feature. For example, a tropical rainforest matches a high-temperature and high-humidity climate, has a high photosynthetic efficiency, and accumulates carbon quickly. For example, an artificial coniferous forest is prone to diseases and pests in a hot and humid area, and has a reduced carbon storage capacity.

[0121] The step S402 is to obtain a temperature feature, a humidity feature, and a disaster feature based on the climate feature.

[0122] Specifically, in the embodiment, the temperature feature includes a regional annual mean temperature, an extreme temperature, a temperature seasonal change, etc. (such as a tropical forest with an annual mean temperature of 25-28°C and a temperate forest with a significant seasonal temperature difference), which directly affects the photosynthesis of plants and the decomposition rate of microorganisms. The humidity feature includes an annual precipitation, a precipitation distribution, an air humidity, etc. (such as a tropical rainforest with an annual precipitation of more than 2000 mm and a forest in an arid area with a precipitation of less than 400 mm), which determines the growth vigor of vegetation and the decomposition speed of litter (a humid environment accelerates decomposition, and a dry environment slows down decomposition). The disaster feature includes climate-related natural disturbances (such as heavy rain, drought, hurricane, and high-incidence period of diseases and pests), which may cause vegetation death or abnormal loss of litter, and significantly reduce the carbon storage in a short term.

[0123] The step S403 is to obtain a vegetation growth suitability degree based on the temperature feature and the humidity feature.

[0124] Specifically, in the embodiment, the vegetation growth suitability degree refers to the favorable degree of the temperature and humidity features to the growth of vegetation, which is usually represented by an index of 0-1 (1 for the most suitable). For example, when the temperature is in the most suitable interval for the photosynthesis of plants (20-25°C for most plants) and the humidity meets the transpiration demand, the suitability degree is high. In a region with a high suitability degree, the vegetation biomass grows quickly, and the vegetation provides more litter and root residues for the soil.

[0125] The step S404 is to obtain a litter generation speed based on the climate matching degree and the vegetation growth suitability degree.

[0126] Specifically, in this embodiment, the litter includes organic matter such as leaves, branches, tree bark and dead vegetation of the vegetation, the litter generation rate refers to the amount of litter generated per unit time, the area with high climate matching degree and high growth suitability has vigorous vegetation growth, the litter generation rate is fast, the litter is the main input source of soil carbon, and the litter generation rate directly affects the soil carbon accumulation efficiency.

[0127] Step S405: Obtain the litter cover thickness of the target forest based on the litter generation rate and the stand age group.

[0128] Specifically, in this embodiment, the litter cover thickness refers to the vertical thickness of the litter accumulated on the ground, and the litter gradually accumulates with the increase of the age (for example, the cover thickness of mature forest can reach 5-10 cm, and the cover thickness of young forest is usually less than 2 cm).

[0129] Step S406: Obtain the carbon storage calibration coefficient based on the disaster characteristics, the litter cover thickness and the external influence characteristics.

[0130] The forest carbon storage calculation method provided by the present embodiment first obtains the climate matching degree based on the vegetation information and the climate characteristics, then extracts the temperature characteristics, the humidity characteristics and the disaster characteristics from the climate characteristics, calculates the vegetation growth suitability according to the temperature characteristics and the humidity characteristics, determines the litter generation rate through the climate matching degree and the vegetation growth suitability, obtains the litter cover thickness in combination with the stand age group, and finally obtains the carbon storage calibration coefficient by comprehensively considering the disaster characteristics, the litter cover thickness and the external influence characteristics; this process builds a multi-dimensional climate and litter dynamic correlation model, comprehensively captures the complex influence of environmental factors on forest carbon cycle, especially through the key intermediate variable of the litter cover thickness, organically combines the climate conditions, the vegetation growth and the external interference, so that the calibration coefficient can more accurately reflect the actual dynamic change of the forest carbon storage, compared with the traditional method which ignores the time-varying influence of environmental factors, the accuracy and adaptability of the carbon storage calculation are significantly improved, and more reliable support is provided for the scientific evaluation of the forest carbon sink function.

[0131] Reference Figure 5 In one of the embodiments of the present embodiment, step S406 obtains the carbon storage calibration coefficient based on the disaster characteristics, the litter cover thickness and the external influence characteristics, including steps S501 to S504:

[0132] Step S501: Obtain the disaster type and the disaster prone time based on the disaster characteristics.

[0133] Specifically, in this embodiment, the disaster type refers to a disaster category classified according to causes and forms, mainly including climate-related disasters and biological disasters, wherein the climate-related disasters include fire (caused by high temperature and drought), storm (caused by strong wind leading to tree uprooting), flood (caused by water accumulation leading to root hypoxia), etc.; the biological disasters include insect pests (such as pine wood nematode disease), diseases (such as anthracnose), etc., which affect carbon accumulation by damaging vegetation.

[0134] Step S502: obtaining a first calibration coefficient based on the disaster type and the disaster-prone time.

[0135] Specifically, the first calibration coefficient refers to a coefficient calculated based on the disaster type and the disaster-prone time for correcting the carbon storage, reflecting the direct or potential loss of the disaster to the carbon pool; in this embodiment, the calculation formula of the first calibration coefficient satisfies wherein u is the number of disaster types (such as fire, insect pests, storm, etc.) that the target forest may encounter; a i is the intensity coefficient of the i th disaster, b i is the disaster-prone time matching degree of the i th disaster; g i is the occurrence frequency coefficient of the i th disaster.

[0136] In this embodiment, the intensity coefficient (0-1) of different disasters can be defined according to the disaster type first, in this embodiment, the greater the damage degree of the disaster, the greater the intensity coefficient corresponding to the disaster; then the corresponding disaster-prone time matching degree (0-1) can be set according to the combination of the disaster-prone time and the growth characteristics and growth cycle of vegetation, reflecting the coincidence degree of the disaster-prone time and the key period of forest carbon accumulation, for example, if the disaster-prone time is in the growing season (peak period of carbon accumulation), the disaster-prone time matching degree is set to 0.8-1.0; if the disaster-prone time is in the dormant period (slow period of carbon accumulation), the disaster-prone time matching degree is set to 0.2-0.4; if there is no obvious time correlation, the disaster-prone time matching degree is set to 0.5; finally, the occurrence frequency coefficient (0-1) of the disaster can be obtained according to the historical data, which is set based on the historical occurrence frequency, high frequency (more than 1 time per year on average), the occurrence frequency coefficient can be set to 0.8-1.0; medium frequency (1 time every 3-5 years), the occurrence frequency coefficient can be set to 0.4-0.6; low frequency (1 time every 10 years or more), the occurrence frequency coefficient can be set to 0.1-0.3.

[0137] Step S503: obtaining a second calibration coefficient based on the litter coverage thickness and the external influence characteristics.

[0138] Specifically, in this embodiment, the second calibration coefficient refers to a coefficient calculated based on the litter coverage thickness and the external influence characteristics, reflecting the influence of external interference on the litter-soil carbon conversion process, and the coefficient is close to 1 when the litter thickness is appropriate and the external interference is small.

[0139] Step S504: obtaining the carbon storage calibration coefficient based on the first calibration coefficient and the second calibration coefficient.

[0140] Specifically, in the embodiment, the carbon storage calibration coefficient is the final correction coefficient obtained by synthesizing 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.

[0141] The forest carbon storage calculation method provided by the embodiment determines the disaster type and the disaster prone time from the disaster characteristics, obtains the first calibration coefficient, obtains the second calibration coefficient in combination with the litter coverage thickness and the external influence characteristics, and finally calculates the carbon storage calibration coefficient through the first calibration coefficient and the second calibration coefficient. Combining the natural factor of disaster with the litter coverage thickness, the external influence characteristics and the like, the calibration coefficient is obtained in layers, which can more accurately reflect the influence of different factors on the carbon storage, thereby improving the accuracy of the forest carbon storage calculation.

[0142] Reference Figure 6 In one of the embodiments of the embodiment, step S503 obtains the second calibration coefficient based on the litter coverage thickness and the external influence characteristics, including steps S601 to S606:

[0143] Step S601: obtaining target biological information and human information based on the external influence characteristics.

[0144] Specifically, in the embodiment, the target biological information and the biological species and characteristics related to the litter dynamics in the target forest focus on the biological species that have a direct effect on the litter, including the species (such as saprophagous insects and herbivorous animals), the quantity (density), the activity habit (such as the frequency of taking litter) and the like; the human information refers to characteristic data related to human activities around the forest, focusing on the human behavior attributes that affect the litter dynamics, including the population, the activity frequency, the lifestyle and the like, which are directly related to the degree of human intervention on the litter.

[0145] Step S602: obtaining the target biological species and the target biological density based on the target biological information.

[0146] Specifically, in the embodiment, the target biological species refers to the biological categories that participate in or affect the litter process, which 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 hares, which directly eat litter or fresh vegetation to reduce the source of litter) and disturbers (such as wild boars, which disturb the soil to destroy the litter cover layer) and the like; the target biological density refers to the number of individuals or the biomass of the target biological in unit area, and the higher the density, the greater the influence on the litter.

[0147] Step S603: Obtain a first influence coefficient of the litter coverage thickness based on the target biological species and the target biological density.

[0148] Specifically, in the embodiment, the first influence coefficient refers to quantifying the action strength of the target biological species on the litter coverage thickness, and reflects the direct influence of biological activities on the litter. In the embodiment, when the decomposer density is high, the coefficient can be greater than 1 (accelerating decomposition and reducing litter thickness); when the consumer density is high, the coefficient can be less than 1 (reducing litter retention). For example, if the earthworm density is extremely high, the first influence coefficient can be 1.2, indicating that the litter decomposition rate is increased by 20%.

[0149] Step S604: Obtain the population density, the human activity frequency, and the lifestyle based on the human information.

[0150] Specifically, in the embodiment, the population density refers to the number of populations in a unit area, reflecting the potential intensity of human activities. In areas with high population density, human disturbance (such as collecting litter for fuel) to the forest is more frequent, and the litter thickness is more significantly affected. The human activity frequency refers to the number of times of human entering the forest or carrying out related activities (such as logging, tourism, and farming) in a unit time, including direct destruction of the litter coverage layer (such as compaction by trampling) or indirect influence on the decomposition environment (such as soil compaction leading to decreased air permeability). The lifestyle refers to the way in which humans rely on forest resources, which can be divided into dependent type and permissive type according to the influence on litter. The dependent type refers to directly using litter (such as for fuel and fertilizer), leading to its artificial loss. The permissive type refers to not directly obtaining litter, but activities (such as camping and road construction) can disturb the coverage state of the litter.

[0151] Step S605: Obtain a second influence coefficient of the litter coverage thickness based on the population density, the human activity frequency, and the lifestyle.

[0152] Specifically, in the embodiment, the second influence coefficient refers to quantifying the action strength of human activities on the litter coverage thickness, and comprehensively reflects the influence of the population density, the activity frequency, and the lifestyle. In the dependent lifestyle, the higher the population density, the smaller the coefficient (the more litter loss). In the permissive lifestyle, the higher the activity frequency, the greater the fluctuation of the coefficient (such as the coefficient may slightly increase when compaction leads to reduced decomposition).

[0153] Step S606: Obtain a second calibration coefficient based on the litter coverage thickness, the first influence coefficient, and the second influence coefficient.

[0154] The forest carbon storage calculation method provided by the embodiment extracts target biological information and human information from external influence characteristics, determines target biological species and density from the target biological information, obtains a first influence coefficient on the litter coverage thickness, obtains population density, human activity frequency and lifestyle from the human information, and obtains a second influence coefficient, and then combines the litter coverage thickness, the first influence coefficient and the second influence coefficient to obtain a second calibration coefficient. By distinguishing the different influences of target organisms and human factors, the action path of external influence characteristics on the litter coverage thickness is refined, the calculation of the second calibration coefficient is more in line with the actual situation, thereby providing support for the accurate acquisition of the carbon storage calibration coefficient and improving the reliability of the forest carbon storage calculation.

[0155] With reference to Figure 7 In one of the embodiments of the present embodiment, step S606 obtains the second calibration coefficient based on the litter coverage thickness, the first influence coefficient and the second influence coefficient, including steps S701 to S703:

[0156] Step S701: If the lifestyle is a dependent lifestyle, obtain the litter demand amount based on the population density.

[0157] Specifically, in the present embodiment, the litter demand amount refers to the total amount of litter obtained by humans in a unit time to meet the living needs, which is determined by the population density and the unit population demand.

[0158] Step S702: Obtain the demand influence coefficient on the litter thickness based on the litter demand amount, and take the demand influence coefficient as the second influence coefficient.

[0159] Specifically, in the present embodiment, the demand influence coefficient refers to the degree of influence of humans on the litter thickness under the dependent lifestyle, which is a parameter for correcting the second influence coefficient, and its calculation formula is:

[0160]

[0161] Wherein, K1 is the demand influence coefficient, W is the population density, A is the unit population annual litter demand amount, Y is the annual litter natural generation amount of the target area, is the litter regeneration compensation coefficient, and μ is the regional adjustment coefficient.

[0162] Step S703: If the lifestyle is a permissive lifestyle, obtain the activity influence coefficient on the litter thickness based on the population density and the activity frequency, and take the activity influence coefficient as the second influence coefficient.

[0163] Specifically, in the present embodiment, the calculation formula of the activity influence coefficient is:

[0164]

[0165] wherein K2 is an activity influence coefficient, is a weight coefficient, and V is an activity frequency, is an activity intensity coefficient, is an activity type attenuation coefficient, and Y is a litter anti-interference threshold.

[0166] The forest carbon storage calculation method provided by the embodiment determines the litter demand amount according to the population density if the lifestyle is dependent, and calculates the demand influence coefficient on the litter thickness by using a demand influence coefficient calculation formula, and takes the demand influence coefficient as the second influence coefficient. If the lifestyle is permissive, the activity influence coefficient on the litter thickness is obtained by using an activity influence coefficient calculation formula based on the population density and the activity frequency, and the activity influence coefficient is taken as the second influence coefficient. The influence of different lifestyles on the litter thickness is calculated in a differentiated manner, and the influence degree is quantified by using specific formulas, so that the determination of the second influence coefficient is more scientific and accurate, and the reliability of the carbon storage calibration coefficient is further improved, thereby helping to improve the accuracy of forest carbon storage calculation.

[0167] Reference Figure 8 In one of the embodiments of the present embodiment, step S107 obtains the forest carbon storage based on the vegetation carbon storage, the soil carbon storage, and the carbon storage calibration coefficient, including steps S801 to S802:

[0168] Step S801: Obtain the comprehensive carbon storage based on the vegetation carbon storage and the soil carbon storage.

[0169] Specifically, in the present embodiment, the comprehensive carbon storage refers to the sum of the vegetation carbon storage and the soil carbon storage, which is the basic total amount of the forest carbon pool and reflects the overall scale of carbon sequestration of the forest ecosystem.

[0170] Step S802: Obtain the forest carbon storage based on the comprehensive carbon storage and the carbon storage calibration coefficient.

[0171] The forest carbon storage calculation method provided by the present embodiment first adds the vegetation carbon storage and the soil carbon storage to obtain the comprehensive carbon storage, and then multiplies the comprehensive carbon storage by the carbon storage calibration coefficient to obtain the forest carbon storage. By first integrating the vegetation and the soil, which are the core parts of forest carbon storage, to obtain the basic total amount, and then combining the calibration coefficient for correction, the main storage carriers of carbon in the forest ecosystem are comprehensively considered, and the possible deviations in the basic calculation are compensated through the calibration mechanism, so that the finally obtained forest carbon storage is more in line with the actual situation, and the accuracy of the calculation result is effectively improved.

[0172] In a second aspect, the present application also discloses a forest carbon storage calculation system.

[0173] Reference Figure 9A forest carbon storage calculation system comprises:

[0174] A first obtaining module is configured to obtain target data corresponding to a target forest;

[0175] A second obtaining module is configured to obtain stand characteristics, climate characteristics and external influence characteristics corresponding to the target forest based on the target data;

[0176] A third obtaining module is configured to obtain forest types, stand densities, stand age groups and soil characteristics based on the stand characteristics;

[0177] A fourth obtaining module is configured to obtain vegetation carbon storage based on the forest types and the stand densities;

[0178] A fifth obtaining module is configured to obtain soil carbon storage based on the soil characteristics and the stand age groups;

[0179] A sixth obtaining module is configured to obtain a carbon storage calibration coefficient based on the climate characteristics and the external influence characteristics;

[0180] A seventh obtaining module is configured to obtain forest carbon storage based on the vegetation carbon storage, the soil carbon storage and the carbon storage calibration coefficient.

[0181] The above are preferred embodiments of the present application, and are not intended to limit the protection scope of the present application, and thus: any equivalent changes made on the structure, shape and principle of the present application shall be encompassed within the protection scope of the present application.

Claims

1. A forest carbon stock calculation method characterized by, The method comprises the following steps: obtaining target data corresponding to a target forest; based on the target data, obtaining stand characteristics, climate characteristics, and external influence characteristics corresponding to the target forest; based on the stand characteristics, obtaining forest types, stand densities, stand age groups, and soil properties; based on the forest types and the stand densities, obtaining vegetation carbon storage; based on the soil properties and the stand age groups, obtaining soil carbon storage; based on the climate characteristics and the external influence characteristics, obtaining a carbon storage calibration coefficient; based on the vegetation carbon storage, the soil carbon storage, and the carbon storage calibration coefficient, obtaining forest carbon storage; wherein the step of obtaining a carbon storage calibration coefficient based on the climate characteristics and the external influence characteristics comprises the following steps: based on vegetation information and the climate characteristics, obtaining a climate matching degree; based on the climate characteristics, obtaining temperature characteristics, humidity characteristics, and disaster characteristics; based on the temperature characteristics and the humidity characteristics, obtaining vegetation growth suitability; based on the climate matching degree and the vegetation growth suitability, obtaining a litter generation speed; based on the litter generation speed and the stand age groups, obtaining a litter cover thickness of the target forest; based on the disaster characteristics, the litter cover thickness, and the external influence characteristics, obtaining a carbon storage calibration coefficient; the step of obtaining a carbon storage calibration coefficient based on the disaster characteristics, the litter cover thickness, and the external influence characteristics comprises the following steps: based on the disaster characteristics, obtaining a disaster type and a disaster prone time; based on the disaster type and the disaster prone time, obtaining a first calibration coefficient; based on the litter cover thickness and the external influence characteristics, obtaining a second calibration coefficient; based on the first calibration coefficient and the second calibration coefficient, obtaining the carbon storage calibration coefficient; the step of obtaining a second calibration coefficient based on the litter cover thickness and the external influence characteristics comprises the following steps: based on the external influence characteristics, obtaining target biological information and human information; based on the target biological information, obtaining a target biological species and a target biological density; based on the target biological species and the target biological density, obtaining a first impact coefficient on the litter cover thickness; based on the human information, obtaining a population density, a human activity frequency, and a lifestyle; based on the population density, the human activity frequency, and the lifestyle, obtaining a second impact coefficient on the litter cover thickness; based on the litter cover thickness, the first impact coefficient, and the second impact coefficient, obtaining the second calibration coefficient; the step of obtaining a second impact coefficient on the litter cover thickness based on the population density, the human activity frequency, and the lifestyle comprises the following steps: if the lifestyle is a dependent lifestyle, obtaining a litter demand amount based on the population density; based on the litter demand amount, obtaining a demand impact coefficient on the litter cover thickness, and taking the demand impact coefficient as the second impact coefficient, the calculation formula of the demand impact coefficient being: Wherein, K1 is the demand impact coefficient, P is the population density, Q is the annual demand of litter per capita, S is the annual natural generation of litter in the target area, is the litter regeneration compensation coefficient, and μ is the regional adjustment coefficient. If the lifestyle is a permissive lifestyle, an activity influence coefficient of the litter cover thickness is obtained based on the population density and the activity frequency, and the activity influence coefficient is taken as the second influence coefficient, and a calculation formula of the activity influence coefficient is: wherein K2 is an activity influence coefficient, is a weight coefficient, F is an activity frequency, is an activity intensity coefficient, is an activity type attenuation coefficient, and T is a litter anti-interference threshold.

2. The method of claim 1, wherein, The vegetation carbon storage is obtained based on the forest type and the stand density, and the obtaining comprises: Based on the forest type, vegetation information of different vegetation in a sample area is obtained; Based on the vegetation information, vegetation hierarchical information, tree survey factors, vegetation density and vegetation coverage are obtained; Based on the vegetation hierarchical information, the tree survey factors, the vegetation density and the vegetation coverage, the vegetation carbon storage is obtained.

3. The method of claim 1, wherein, The soil carbon storage is obtained based on the soil characteristics and the stand age group, and the obtaining comprises: Based on the soil characteristics, soil type and soil fertility are obtained; Based on the soil fertility and the stand age group, soil biological density is obtained; Based on the soil type, the soil fertility and the soil biological density, the soil carbon storage is obtained.

4. The method of claim 1, wherein, The forest carbon storage is obtained based on the vegetation carbon storage, the soil carbon storage and the carbon storage calibration coefficient, and the obtaining comprises: Based on the vegetation carbon storage and the soil carbon storage, a comprehensive carbon storage is obtained; Based on the comprehensive carbon storage and the carbon storage calibration coefficient, the forest carbon storage is obtained.

5. A forest carbon stock calculation system for performing the method of any one of claims 1 to 4, characterized by, The method comprises: A first obtaining module is configured to obtain target data corresponding to a target forest; A second obtaining module is configured to obtain stand characteristics, climate characteristics and external influence characteristics corresponding to the target forest based on the target data; A third obtaining module is configured to obtain forest type, stand density, stand age group and soil characteristics based on the stand characteristics; A fourth obtaining module is configured to obtain vegetation carbon storage based on the forest type and the stand density; A fifth obtaining module is configured to obtain soil carbon storage based on the soil characteristics and the stand age group; A sixth obtaining module is configured to obtain a carbon storage calibration coefficient based on the climate characteristics and the external influence characteristics; A seventh obtaining module is configured to obtain forest carbon storage based on the vegetation carbon storage, the soil carbon storage and the carbon storage calibration coefficient.

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