Forest carbon sink potential big data prediction method

Abstract

The invention discloses a forest carbon sink potential big data prediction method, and relates to the technical field of environmental science, and the main scheme is as follows: collecting multiple data of a forest; calculating tree growth state indexes; calculating the microbial biomass carbon content of the soil, and further obtaining the total carbon reserve of the soil; calculating a vegetation coverage factor and a vegetation coverage degree, and further calculating a forest canopy density; calculating an environmental influence index; calculating a comprehensive index, and obtaining a forest ecological activeness index; calculating a forest carbon sink sustainability index, and presetting a threshold value to judge the sustainability level of the forest; calculating a preset threshold value of a forest carbon sink potential evaluation value, judging a forest carbon sink potential grade, and taking a corresponding forest management measure; according to the method, the forest ecosystem can be comprehensively evaluated, the sustainability and the carbon sink potential level of the forest can be accurately judged, and then scientific and reasonable management and protection measures are taken.

Description

Technical Field

[0001] The present invention relates to the field of environmental science and technology, and specifically to a big data prediction method for forest carbon sink potential. Background Art

[0002] Forest carbon sinks play a crucial role in regulating the global climate. Accurately assessing forest carbon sink potential is the key to reasonably planning forest resource management and formulating effective strategies to address climate change. However, forest carbon sink potential is affected by multiple factors, such as the complexity of forest ecosystems, the variability of climate conditions, the diversity of soil characteristics, etc. These factors interact with each other, making it a huge challenge to accurately assess forest carbon sink potential.

[0003] When existing technologies solve the problem of predicting forest carbon sink potential, they usually rely on the collection and analysis of forest-related data. On the one hand, basic data of forests are obtained through field investigations. On the other hand, remote sensing technology is used to obtain large-area forest vegetation information.

[0004] Existing technologies have certain defects in predicting forest carbon sink potential. For example, existing technologies are insufficient in considering the complex interactions within forest ecosystems. Forest carbon sinks involve the mutual influence of multiple links such as vegetation, soil, and microorganisms. Current methods often cannot fully consider the mutual influence relationships between these links. Summary of the Invention

[0005] (1) Technical Problems to be Solved In view of the deficiencies of the existing technologies, the present invention provides a big data prediction method for forest carbon sink potential. By collecting multiple data of forests; calculating tree growth status indicators; calculating the soil microbial biomass carbon content, and further obtaining the total soil carbon storage; calculating the vegetation cover factor and vegetation coverage, and then calculating the forest canopy density; calculating environmental impact indicators; calculating comprehensive indicators, and obtaining the forest ecological activity index; calculating the forest carbon sink sustainability index, presetting thresholds to judge the sustainability level of the forest; calculating the forest carbon sink potential evaluation value preset threshold and judging the forest carbon sink potential level, and taking corresponding forest management measures, the problems of one-sidedness in traditional forest carbon sink potential evaluation and inaccurate carbon sink potential prediction are solved.

[0006] (2) Technical Solutions To achieve the above objectives, the present invention is realized through the following technical solutions: A big data prediction method for forest carbon sink potential, including: Step 1: Collect the growth parameter data, soil data, meteorological data, and forest resource data of the forest; Step 2: Calculate the tree growth status indicators according to the growth parameter data, soil data, and climate data ; Calculate the soil microbial biomass carbon content based on soil data , calculate the total soil carbon storage according to the soil microbial biomass carbon content and soil data ; Calculate the vegetation cover factor based on growth parameter data , calculate the vegetation coverage according to the vegetation cover factor and forest resource data ; Calculate the forest canopy density according to the vegetation coverage and forest resource data ; Calculate the environmental impact index based on meteorological data ; Step 3: Calculate the comprehensive index according to the tree growth status index , total soil carbon storage , forest canopy density and environmental impact index ; Calculate the forest ecological activity index according to the comprehensive index , soil data and meteorological data ; ; Step 4: Calculate the forest carbon sink sustainability index according to the forest ecological activity index , growth parameter data and meteorological data ; Preset the forest carbon sink sustainability index threshold, compare the forest carbon sink sustainability index with the forest carbon sink sustainability index threshold, judge the sustainability level of the forest according to the result, and take corresponding protection and restoration measures; Calculate the forest carbon sink potential evaluation value according to the forest carbon sink sustainability index , total soil carbon storage and meteorological data ; Preset the forest carbon sink potential evaluation value threshold, compare the forest carbon sink potential evaluation value with the forest carbon sink potential evaluation value threshold, judge the forest carbon sink potential level according to the result, and take corresponding forest management measures.

[0007] In the preferred scheme of the above forest carbon sink potential big data prediction method: The method for calculating the tree growth status index is as follows: The growth parameter data includes the mean diameter at breast height , mean tree height , mean crown width , mean tree age and mean tree branch angle ; The soil data includes the mean soil water content ; Meteorological data includes the average wind speed ; According to the average diameter at breast height , the average tree height , the average crown width , the average tree age , the average tree branch angle , the average soil water content and the average wind speed , calculate the tree growth status index , and the formula is:

[0008] where e is the natural constant; is the average slope.

[0009] In the preferred scheme of the above method for predicting forest carbon sink potential from big data: The method for calculating the total soil carbon storage is: Soil data also includes the soil microbial respiration frequency , the soil organic carbon content and the soil porosity ; Based on the soil microbial respiration frequency , calculate the soil microbial biomass carbon content , and the calculation formula is:

[0010] where, is the conversion factor; is the empirical coefficient; According to the soil microbial biomass carbon content , the soil organic carbon content and the soil porosity , calculate the total soil carbon storage , and the formula is:

[0011] where, is the microbial decomposition coefficient.

[0012] In the preferred scheme of the above method for predicting forest carbon sink potential from big data: The method for calculating the forest canopy density is: Growth parameter data also includes the vegetation growth density , the average vegetation growth height and the average vegetation growth diameter ; Forest resource data includes the total area of the forest area ; According to the vegetation growth density , the average vegetation growth height and the average vegetation growth diameter , calculate the vegetation coverage factor , and the calculation formula is:

[0013] where, is the vegetation growth density of the i-th vegetation type; is the average vegetation growth height of the i-th vegetation type; is the average vegetation growth diameter of the i-th vegetation type; is the vegetation complexity coefficient of the i-th vegetation type; According to the vegetation coverage factor and the total area of the forest area , calculate the vegetation coverage , and the calculation formula is:

[0014] where, is the area of the i-th vegetation type in the forest area, i is the serial number of the vegetation type, and the value range is [1, n]; n is the number of different vegetation types in the forest area, and the value range is a positive integer; According to the vegetation coverage and the total area of the forest area , calculate the forest canopy density , and the formula is:

[0015] where, is the average crown area of the trees in the forest.

[0016] In the preferred scheme of the above-mentioned method for predicting big data of forest carbon sequestration potential: The method for calculating the environmental impact index is: The meteorological data also includes the annual average temperature , the annual precipitation , the accumulated temperature and the temperature change rate ; According to the annual average temperature , the annual precipitation , the accumulated temperature and the temperature change rate , calculate the environmental impact index , and the formula is:

[0017] where, is the weight coefficient of , with a value range of 0.2 - 0.4; is the weight coefficient of , with a value range of 0.1 - 0.3; is the weight coefficient of , with a value range of 0.3 - 0.5; is the weight coefficient of , with a value range of 0.2 - 0.4; and .

[0018] In the preferred scheme of the above - mentioned big - data prediction method for forest carbon sink potential: The method for calculating the comprehensive index is as follows: According to the tree growth status index , the total soil carbon storage , the forest canopy density and the environmental impact index , calculate the comprehensive index , and the specific formula is:

[0019] Among them, is the weight coefficient of the tree growth status index , with a value range of 0.2 - 0.3; is the weight coefficient of the total soil carbon storage , with a value range of 0.2 - 0.4; is the weight coefficient of the forest canopy density , with a value range of 0.3 - 0.4; is the weight coefficient of the environmental impact index , with a value range of 0.1 - 0.3; and .

[0020] In the preferred scheme of the above - mentioned big - data prediction method for forest carbon sink potential: The method for calculating the forest ecological activity index is as follows: The soil data also includes the soil area ; According to the comprehensive index , the soil area and the temperature change rate , calculate the forest ecological activity index , and the formula is:

[0021] Among them, is the species richness; is the ecological value assessment value; is the leaf area index; is the moisture content of the forest.

[0022] In the preferred embodiment of the above-mentioned big data prediction method for forest carbon sink potential: calculating the forest carbon sink sustainability index The method is as follows: Based on the mean tree age , forest ecological activity index and annual precipitation , calculate the forest carbon sink sustainability index , and the formula is:

[0023] where, is the forest area growth rate; is the forest deforestation rate; is the forest degradation rate.

[0024] In the preferred embodiment of the above-mentioned big data prediction method for forest carbon sink potential: the method for judging the sustainability level of the forest is as follows: The forest carbon sink sustainability index threshold includes the forest carbon sink sustainability index threshold one and the forest carbon sink sustainability index threshold two , and ; When , judge that the sustainability level of the forest is low, and take protection and restoration measure one; When , judge that the sustainability level of the forest is medium, and take protection and restoration measure two; When , judge that the sustainability level of the forest is high, and take protection and restoration measure three.

[0025] In the preferred embodiment of the above-mentioned big data prediction method for forest carbon sink potential: the method for judging the forest carbon sink potential level is as follows: Based on the forest carbon sink sustainability index , total soil carbon storage and temperature change rate , calculate the forest carbon sink potential evaluation value , and the formula is:

[0026] where, is the soil bulk density; The forest carbon sink potential evaluation value threshold includes the forest carbon sink potential evaluation value threshold one and the forest carbon sink potential evaluation value threshold two , and ; When occurs, it is determined that the forest carbon sink potential level is low, and forest management measure one is taken; When occurs, it is determined that the forest carbon sink potential level is medium, and forest management measure two is taken; When occurs, it is determined that the forest carbon sink potential level is high, and forest management measure three is taken.

[0027] (III) Beneficial Effects The present invention provides a big data prediction method for forest carbon sink potential, having the following beneficial effects: (1) Growth parameter data can reflect the growth status of trees, which helps to understand the growth vitality of forests; soil data can reveal the fertility, structure, etc. of the soil; meteorological data can present environmental factors such as temperature, precipitation, wind speed, etc., which directly affect the growth and carbon sink function of forests; forest resource data can clarify the area, tree species composition and distribution of forests, providing a basis for subsequent resource management and protection.

[0028] (2) Calculating the growth status index can accurately grasp the growth health of each tree and even the entire forest. Calculating the soil microbial biomass carbon content and the total soil carbon storage can provide an important basis for studying the forest carbon sink function and can also provide a reference for soil fertility management. Calculating the vegetation coverage and forest canopy density can reflect the vegetation density of the forest, which is of great value for evaluating the ecological service function of the forest. And calculating the environmental impact index can reflect the impact of meteorological factors on the forest and better understand the interaction relationship between the forest and the climate.

[0029] (3) Calculating the comprehensive index can integrate multi-dimensional information such as tree growth status, soil carbon storage, forest canopy density and environmental impact into a single value for comprehensively evaluating the forest ecosystem, which is convenient for horizontal comparison of different forest areas. Calculating the forest ecological activity index can more deeply reflect the vitality and functional state of the forest ecosystem and can judge whether the forest ecosystem is in a healthy and active state.

[0030] (4) Calculating the forest carbon sink sustainability index and comparing it with the threshold can clarify the sustainable development level of the forest in terms of carbon sink, and then judge the sustainability level of the forest. Taking corresponding protection and restoration measures according to different levels can scientifically manage the forest targeted and ensure the long-term stability of the forest carbon sink function. Calculating the forest carbon sink potential evaluation value and comparing it with the threshold can reveal the potential capacity of the forest in carbon absorption and storage. Taking corresponding forest management measures according to the forest carbon sink potential level can optimize the forest management strategy and fully tap the forest carbon sink potential. Description of the Drawings

[0031] Figure 1 This is a schematic diagram of the steps of a big data prediction method for forest carbon sink potential of the present invention; Figure 2 This is a schematic diagram of the working steps of Step 3 in the big data prediction method for forest carbon sink potential of the present invention; Figure 3 This is a schematic diagram of the working steps of Step 4 in the big data prediction method for forest carbon sink potential of the present invention. Detailed implementation manners

[0032] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0033] Please refer to Figures 1 - 3 , the present invention provides a big data prediction method for forest carbon sink potential, including: Step 1: Collect growth parameter data, soil data, meteorological data, and forest resource data of the forest.

[0034] Comprehensive Step 1: The growth parameter data can intuitively display the growth trend of trees, helping to understand the natural regeneration ability and biomass accumulation of the forest; the soil data provides a basis for evaluating soil fertility, nutrient cycling, and the carbon fixation and release ability of the soil; the meteorological data is crucial for analyzing the impact of climate on the forest, directly related to the growth of trees and the stability of the forest ecosystem; the forest resource data can clearly present the distribution, area, tree species composition, etc. of the forest, providing a basis for the reasonable planning of the utilization and protection of forest resources.

[0035] Step 2: Calculate the tree growth status index according to the growth parameter data, soil data, and climate data ; Calculate the soil microbial biomass carbon content based on the soil data , and calculate the total soil carbon storage according to the soil microbial biomass carbon content and the soil data ; Calculate the vegetation cover factor based on the growth parameter data , and calculate the vegetation coverage according to the vegetation cover factor and the forest resource data ; Calculate the forest canopy density according to the vegetation coverage and the forest resource data ; Calculate the environmental impact index based on the meteorological data 。

[0036] Step 201: Calculate the tree growth status indicators , and the specific steps are as follows: The growth parameter data includes the mean diameter at breast height , the mean tree height , the mean crown width , the mean tree age and the mean tree branch angle .

[0037] Select several -meter square plots in a grid pattern in the forest area as the research area; this can encompass different terrains and reduce the limitations of sampling as much as possible.

[0038] It should be noted that in the research area, the diameter of the tree trunk is measured at 1.3 meters above the ground using a diameter tape to obtain the diameter at breast height of the tree. Repeat the above measurement process for all trees in the research area, add up all the measured values of the diameter at breast height of the trees, and then divide by the total number of trees measured to obtain the mean diameter at breast height .

[0039] In the research area, using a laser altimeter, measure the horizontal distance from the measurement point to the tree base and the elevation angle from the measurement point to the tree top , and calculate the tree height as ; repeat the above measurement process for all trees in the research area, add up all the measured values of the tree height of the trees, and then divide by the total number of trees measured to obtain the mean tree height .

[0040] In the research area, use a tape measure to measure the maximum width of the tree crown in the east-west direction and the maximum width in the north-south direction , then calculate the crown width of the tree as ; repeat the above measurement process for all trees in the research area, add up all the measured values of the crown width of the trees, and then divide by the total number of trees measured to obtain the mean crown width .

[0041] In the research area, use an increment borer to take a core sample from the tree trunk, observe the tree rings to determine the tree age. Repeat the above measurement process for all trees in the research area, add up all the measured values of the tree age of the trees, and then divide by the total number of trees measured to obtain the mean tree age .

[0042] In the study area, the angles between multiple main branches of trees and the tree trunks were measured using a goniometer to obtain multiple branch angle values. The above measurement process was repeated for all trees in the study area, and all the measured branch angle values were added together and then divided by the total number of measured angles to obtain the mean tree branch angle. .

[0043] The soil data includes the mean soil water content. .

[0044] It should be noted that in different study areas, by vertically inserting the TRD probe instrument into the soil, the instrument will emit electromagnetic waves and measure the propagation time in the soil, and can automatically calculate the soil water content of each sample plot study area. Add up all the measured soil water content values and then divide by the number of measurements to obtain the mean soil water content. .

[0045] The meteorological data includes the mean wind speed. .

[0046] It should be noted that in different study areas, by using a three-cup anemometer, the wind speed of the sample plot study area was measured separately. Add up all the measured wind speed values and then divide by the number of measurements to obtain the mean wind speed. .

[0047] According to the mean diameter at breast height , the mean tree height , the mean crown width , the mean tree age , the mean tree branch angle , the mean soil water content and the mean wind speed , calculate the tree growth status index , and the formula is:

[0048] Among them, e is the natural constant, and its value is 2.71828; is the mean slope.

[0049] It should be noted that to obtain the mean slope , by using a level, two measurement points were selected in each study area, one as the reference point and one as the target point. Install the level at the reference point and set up the leveling staff at the target point. Read the reading on the leveling staff through the level to obtain the elevation difference between the two points. Use a steel tape to measure the horizontal distance between the two measurement points, and divide the elevation difference by the horizontal distance to obtain the slope value of this sample plot study area. Repeat the above operation for the slopes of all study areas, add up all the measured slope values, and then divide by the number of measurements to obtain the mean slope. .

[0050] It should be noted that Combined with the mean DBH, mean tree height, mean crown width, mean tree age, and mean tree branch angle, it reflects the basic growth status of the trees. It reflects that the higher the soil water content, the larger this value, and the greater the contribution to the tree growth status indicators. It indicates that wind speed has a negative impact on tree growth. It indicates that the larger the slope, the smaller this value, and the smaller the contribution to the tree growth status indicators. The denominator is for normalizing the numerator to keep the calculation result within a reasonable range.

[0051] Step 202: Calculate the total soil carbon storage , and the specific steps include: The soil data also includes the soil microbial respiration frequency , the soil organic carbon content , and the soil porosity .

[0052] It should be noted that in different research areas, by inserting the probe of the in-situ respiration meter into the soil to make the probe in close contact with the soil; the instrument automatically records the amount of carbon dioxide released by the soil within a certain period of time, and the microbial respiration frequency values in different research areas are automatically calculated through the built-in program. Add up all the measured microbial respiration frequency values and then divide by the number of measurements to obtain the soil microbial respiration frequency of the forest .

[0053] In different research areas, by placing the soil respiration chamber on the soil surface, connecting the gas sampling tube and the portable CO2 analyzer, and measuring the change in CO2 concentration in the chamber at certain time intervals; according to the air volume in the chamber, the measurement time interval, and the change in CO2 concentration, the instrument automatically gives the soil organic carbon content in different research areas. Add up all the measured soil organic carbon contents and then divide by the number of measurements to obtain the soil organic carbon content of the forest .

[0054] In different research areas, by collecting the volume of undisturbed soil samples and putting them into a container; slowly adding water to the container until the soil is completely saturated with water, and recording the volume of water added as , and calculate the soil porosity in different research areas as . Add up all the measured soil porosities and then divide by the number of measurements to obtain the soil porosity of the forest .

[0055] Based on the soil microbial respiration frequency , calculate the soil microbial biomass carbon content , the calculation formula is:

[0056] Among them, is the conversion factor, and its value is 38; is the empirical coefficient.

[0057] It should be noted that the conversion factor can be obtained by referring to technical books such as "Principles and Applications of Soil Microbiology" to obtain the value of the conversion factor , and the value can be 38.

[0058] It should be noted that is the empirical coefficient used in the calculation method of soil microbial biomass, which can be obtained by referring to technical books such as "Principles and Applications of Soil Microbiology", and the value here is 2.64. The empirical coefficient and the conversion factor act together to convert the measured microbial respiration frequency into soil microbial biomass carbon .

[0059] According to the soil microbial biomass carbon content , soil organic carbon content and soil porosity , calculate the total soil carbon storage , and the formula based on is:

[0060] Among them, is the microbial decomposition coefficient.

[0061] It should be noted that to obtain the microbial decomposition coefficient , collect soil samples in forest areas, place the soil samples in petri dishes, and add the initial amounts of labeled substrates such as glucose and cellulose containing radioactive carbon or stable isotopes to the soil samples ; regularly use a gas analyzer to measure the amount of carbon dioxide released in the soil , and obtain the decomposition rate of organic matter through the initial amount of the labeled substrate and the amount of carbon dioxide produced by decomposition , . Then calculate the microbial decomposition coefficient , , among which, is the soil microbial biomass.

[0062] It should be noted that The part represents the contribution of soil microbial biomass carbon to the total soil carbon storage. It represents the non-linear contribution of soil organic carbon content to the total carbon storage. It indicates that as the porosity increases, the rates of microbial activity and organic carbon decomposition change. When the porosity is small, microbial activity is restricted, organic carbon decomposition is slow, and the contribution to the total carbon storage is large. This term takes into account the synergistic effect of soil organic carbon content and soil porosity. There is an interactive relationship between soil organic carbon and porosity. Larger porosity can provide more storage space for organic carbon, and at the same time, organic carbon also affects the soil structure and porosity. It reflects the impact of this synergistic effect on the total soil carbon storage. By comprehensively considering factors such as soil microbial biomass carbon, soil organic carbon content, and soil porosity and their interrelationships, this formula can more comprehensively estimate the total soil carbon storage. .

[0063] Step 203: Calculate the forest canopy density , and the specific steps include: The growth parameter data also includes the vegetation growth density , the average vegetation growth height and the average vegetation growth diameter. .

[0064] It should be noted that satellite remote sensing images or aerial remote sensing images of the forest area are obtained. Through image interpretation and classification techniques, different vegetation types are identified, and pixel statistics are carried out on the vegetation-covered area to obtain the number of pixels. . According to the known ground resolution rr, the vegetation growth density is calculated , , where is the number of pixels of the i-th vegetation type.

[0065] By using a drone equipped with lidar for vegetation height measurement, lidar can quickly obtain vegetation height data over a large area, and the average vegetation growth height is obtained through point cloud data processing. .

[0066] By using a drone equipped with a camera to conduct aerial photography of the forest area, orthophoto images of the forest area are generated through operations such as stitching and orthorectification; through image analysis software, vegetation individuals can be identified, and the projected diameter of each on the image can be measured; the measured diameter values of all identified vegetation individuals are statistically averaged to obtain the average vegetation growth diameter. .

[0067] The forest resource data includes the total area of the forest area. .

[0068] It should be noted that according to the archival records of forest resources recorded by the forestry department, the area record of the target forest area is found to obtain the total area of the forest area. .

[0069] According to the vegetation growth density , the average vegetation growth height and the average vegetation growth diameter , the vegetation coverage factor is calculated , and the calculation formula is:

[0070] Wherein, is the vegetation growth density of the i-th vegetation type; is the average vegetation growth height of the i-th vegetation type; is the average vegetation growth diameter of the i-th vegetation type; is the vegetation complexity coefficient of the i-th vegetation type.

[0071] It should be noted that to obtain the vegetation complexity coefficient of the i-th vegetation type , the L-system growth model is selected. According to the growth characteristics and environmental conditions of the vegetation type, the model parameters are set for simulated growth. The fractal dimension is calculated based on the plant images generated by the simulation, and the calculated fractal dimension is the vegetation complexity coefficient of the vegetation type. .

[0072] It should be noted that this formula comprehensively considers the vegetation growth density , the average vegetation growth height and the average vegetation growth diameter . After multiplying these three factors and dividing by the vegetation complexity coefficient , the vegetation coverage factor is obtained. In this way, the formula comprehensively considers the influence of the quantity, height, size and morphological structure of the vegetation on the vegetation coverage degree, so as to more accurately quantify the contribution of each vegetation type to the overall vegetation coverage.

[0073] According to the vegetation coverage factor and the total area of the forest area , the vegetation coverage is calculated , and the calculation formula is:

[0074] Wherein, is the area of the i-th vegetation type in the forest area, i is the serial number of the vegetation type, and the value range is [1, n]; n is the number of different vegetation types in the forest area, and the value range is a positive integer.

[0075] It should be noted that to obtain the area of the i-th vegetation type in the forest area , by obtaining satellite remote sensing images or aerial remote sensing images of the forest area, using image interpretation and classification techniques, different vegetation types are identified, and pixel statistics are performed on the vegetation-covered area to obtain the number of pixels . According to the known ground resolution rr, the area of the i-th vegetation type in the forest area is calculated , .

[0076] It should be noted that the comprehensive influence of the coverage factors of different vegetation types and their areas in the region is considered, so as to more accurately reflect the vegetation coverage of the ground. It is to sum up the area ratios of all vegetation types in the forest area. This is actually a normalization process for the proportion of the area occupied by vegetation in the entire forest area, ensuring that the calculation results are within a reasonable range. In this way, the coverage characteristics of different vegetation types and their distribution in the forest area are comprehensively considered, and an index that can accurately reflect the vegetation coverage degree is obtained.

[0077] According to the vegetation coverage and the total area of the forest area , the forest canopy density is calculated , and the formula is:

[0078] where is the average crown area of trees in the forest.

[0079] It should be noted that to obtain the average crown area of trees in the forest , in each study area, a laser rangefinder is used to measure the maximum width of each tree's crown in the east-west direction and the maximum width in the north-south direction , then the crown width of the tree is ; the crown areas of all the measured trees are added together, and then divided by the total number of measured trees to obtain the average crown area of trees in the forest .

[0080] It should be noted that this part represents considering the proportion of the vegetation coverage and the average crown area of trees relative to the total forest area. This part is an adjustment factor. It represents the ratio of the difference between the forest area and the average crown area of trees to the average crown area of trees, and then multiplied by After that, add the sum of 1 to adjust the result calculated previously.

[0081] Step 204: Calculate the environmental impact index , and the specific steps include: The meteorological data also includes the annual average temperature , annual precipitation , accumulated temperature and temperature change rate .

[0082] It should be noted that by placing a thermometer in a meteorological observation box and measuring the temperature data once an hour, continuously recording the temperature data for a whole year, adding up all the recorded temperature data, and then dividing by the number of records, the annual average temperature can be obtained .

[0083] By installing a rain gauge in the forest area, the rain gauge will automatically record the precipitation every time it rains; adding up the precipitation each time within a year, the annual precipitation can be obtained .

[0084] Use a thermometer to obtain the daily average temperature of the forest area , then calculate the accumulated temperature , and the calculation formula is:

[0085] Among them, is the biological zero degree; is the daily average temperature on the dth day, d is the serial number of the measurement days, and the value range is [1, m]; m is the total number of measurement days, and the value is a positive integer.

[0086] Obtain the continuous temperature time series within the forest area every day, and calculate the temperature difference between each day and the previous day; add up the absolute values of these temperature differences, and then divide by the number of time intervals to obtain the temperature change rate .

[0087] According to the annual average temperature , annual precipitation , accumulated temperature and temperature change rate , calculate the environmental impact index , and the formula based on it is:

[0088] Among them, is 's weight coefficient, and the value range is 0.2 - 0.4; is 's weight coefficient, and the value range is 0.1 - 0.3; is The weight coefficient ranges from 0.3 to 0.5; is The weight coefficient of, ranges from 0.2 to 0.4; and .

[0089] It should be noted that this formula comprehensively considers the annual average temperature , annual precipitation , accumulated temperature and temperature change rate These four important parameters. Each parameter has a corresponding weight coefficient, indicating the degree of importance attached to different parameters when calculating the environmental impact index . By multiplying each parameter by its corresponding weight coefficient and then summing, the environmental impact index is obtained.

[0090] Comprehensive steps 201 to 204: Calculating the tree growth status index can accurately evaluate the growth trend of each tree, helping to promptly detect trees with poor growth. The calculation of soil microbial biomass carbon content and soil total carbon storage provides an effective basis for evaluating soil fertility and formulating reasonable soil management measures. The calculation of vegetation coverage and forest canopy density can intuitively reflect the vegetation density of the forest. Calculating the environmental impact index can reflect the comprehensive impact of climate factors on the forest, providing scientific data support for the forest to respond to climate change.

[0091] Step three: According to the tree growth status index , soil total carbon storage , forest canopy density and environmental impact index , calculate the comprehensive index ; According to the comprehensive index , soil data and meteorological data, calculate the forest ecological activity index .

[0092] Step 301: Calculate the comprehensive index , and the specific steps include: According to the tree growth status index , soil total carbon storage , forest canopy density and environmental impact index , calculate the comprehensive index , and the specific formula is:

[0093] Among them, is the weight coefficient of the tree growth status index , and its value ranges from 0.2 to 0.3; is the total soil carbon storage is the weight coefficient, with a value ranging from 0.2 to 0.4; is the forest canopy density is the weight coefficient, with a value ranging from 0.3 to 0.4; is the environmental impact index is the weight coefficient, with a value ranging from 0.1 to 0.3; and .

[0094] It should be noted that this formula comprehensively considers the tree growth status index , the total soil carbon storage , the forest canopy density and the environmental impact index These four important parameters. Each parameter has a corresponding weight coefficient, indicating the degree of importance attached to different parameters when calculating the comprehensive index . By multiplying each parameter by its corresponding weight coefficient and then summing them up, the comprehensive index is obtained.

[0095] Step 302: Calculate the forest ecological activity index , and the specific steps include: The soil data also includes the soil area .

[0096] It should be noted that by obtaining the satellite remote sensing image of the forest area and using image classification technology to extract the soil area from the image; calculating the area of the extracted soil area, the software can directly output the area data of this area to obtain the soil area .

[0097] According to the comprehensive index , the soil area and the temperature change rate , calculate the forest ecological activity index , and the formula is:

[0098] Among them, is the species richness, with the unit of "species"; is the ecological value assessment value, and the unit can be different currency values, such as "yuan, US dollar"; is the leaf area index, with the unit of "square meter"; is the moisture content of the forest, with the unit of "percentage".

[0099] It should be noted that obtain the species richness , by collecting biological samples in different research areas, such as plant leaves, animal feces, and soil microorganisms, extracting DNA samples, identifying species through DNA barcoding technology, counting the number of identified species, and summarizing the number of species recorded in all plots to obtain species richness 。

[0100] Obtain the ecological value assessment , by using the ARIES ecosystem assessment model, inputting relevant parameters of the forest, such as vegetation type, topography, and climate conditions, etc., after the model runs, obtain the ecological value assessment of the forest in multiple ecological service functions 。

[0101] Obtain the leaf area index , select multiple trees of different tree species in different research areas, and collect leaf samples at different heights of the tree crown, such as the top, middle, bottom, and different directions such as east, south, west, and north. Use a leaf area meter to directly measure the area of each leaf, and then add up the areas of all leaves to obtain the total leaf area. Measure the land area corresponding to the collected leaf samples and calculate the leaf area index , = total leaf area / land area.

[0102] Obtain the moisture content of the forest , collect soil samples at different locations in different research areas, put the soil samples into an oven, dry them to a constant weight at 105 °C, and calculate the soil moisture content according to the weight difference before and after drying , = (wet soil weight - dry soil weight) / wet soil weight × 100%.

[0103] It should be noted that represents the comprehensive impact of the comprehensive index, species richness, and ecological value assessment, and dividing by 10000 is to adjust the value range to make its weight appropriate in the whole formula. represents the comprehensive impact of the leaf area index and moisture content relative to the relevant area, reflecting the situation of the forest in terms of water use and vegetation cover. represents a positive adjustment to the overall activity on the basis of considering temperature stability.

[0104] Integrate Step 301 to Step 302: The comprehensive index integrates information on tree growth status, soil carbon storage, forest canopy density, and environmental impacts, etc., to form a single value that can comprehensively reflect the status of the forest ecosystem, facilitating horizontal comparison and comprehensive assessment of different forest areas. The calculation of the forest ecological activity index further considers soil and meteorological data, can accurately reflect the inherent vitality and functional status of the forest ecosystem, and helps to long-term monitor the health status of the forest ecosystem.

[0105] Step Four: According to the forest ecological activity index , growth parameter data, and meteorological data, calculate the forest carbon sink sustainability index ; preset the threshold of the forest carbon sink sustainability index, compare the forest carbon sink sustainability index with the threshold of the forest carbon sink sustainability index, judge the sustainability level of the forest according to the result, and take corresponding protection and restoration measures; according to the forest carbon sink sustainability index , total soil carbon storage and meteorological data, calculate the forest carbon sink potential assessment value ; preset the threshold of the forest carbon sink potential assessment value, compare the forest carbon sink potential assessment value with the threshold of the forest carbon sink potential assessment value, judge the forest carbon sink potential level according to the result, and take corresponding forest management measures.

[0106] Step 401: Calculate the forest carbon sink sustainability index , and the specific steps include: According to the mean tree age , the forest ecological activity index and the annual precipitation , calculate the forest carbon sink sustainability index , and the formula is:

[0107] Among them, is the forest area growth rate; is the forest deforestation rate; is the forest degradation rate.

[0108] It should be noted that to obtain the forest area growth rate , the change in forest area is obtained through satellite remote sensing images, and the forest area at time and the forest area at time are obtained, and the forest area growth rate is calculated, and the calculation formula is: .

[0109] Obtain the deforestation rate , set fixed sample plots in the forest, and regularly monitor the tree cutting situation in the sample plots. Obtain the deforested area at time and the deforested area at time and calculate the deforestation rate and the deforested area at time , and calculate the deforestation rate . The calculation formula is: .

[0110] Obtain the forest degradation rate , through on-site measurement and sampling, obtain the biomass at time and the biomass at time and calculate the forest degradation rate and the biomass at time , then calculate the forest degradation rate . The calculation formula is:

[0111] Among them, the biomass at time refers to the total biomass in the forest ecosystem at time . This biomass includes the mass of all biological components in the forest, such as trees, shrubs, herbs, microorganisms, and animals, etc. The biomass at time refers to the total biomass in the forest ecosystem after a period of time from to . This biomass reflects the change in biomass in the forest during this period. to

[0112] It should be noted that this ratio reflects the dynamic change of the forest area. This term represents the reciprocal of the forest degradation rate. The lower the forest degradation rate, the larger the value of , indicating that the quality and function of the forest are better maintained, which is beneficial to the sustainability of carbon sinks. This term considers the impact of precipitation on forest carbon sinks.

[0113] Step 402: Judge the sustainability level of the forest. The specific steps include: The forest carbon sink sustainability index threshold includes the forest carbon sink sustainability index threshold one ​and the threshold two of the forest carbon sink sustainability index , and ; by collecting the ecological data of other forests in normal ecology, including the forest ecological activity index , annual precipitation , forest area growth rate , forest deforestation rate , forest degradation rate and the average tree age of the forest and other parameters, calculate each forest carbon sink sustainability index ; calculate the average value and the standard deviation of the forest carbon sink sustainability indexes of these forests, and take as the threshold one of the forest carbon sink sustainability index ; take as the threshold two of the forest carbon sink sustainability index .

[0114] When , determine that the sustainability level of the forest is low, and take protection and restoration measures one; protection and restoration measures one are to immediately stop all illegal forest cutting activities, strengthen forest law enforcement, and ensure that forest resources are strictly protected; implement forest restoration plans such as afforestation and forest tending.

[0115] When , determine that the sustainability level of the forest is medium, and take protection and restoration measures two; protection and restoration measures two are to adjust the forest cutting quota, preferentially cut mature forests; strengthen the prevention and control of forest pests and fires.

[0116] When , determine that the sustainability level of the forest is high, and take protection and restoration measures three; protection and restoration measures three are to continue to maintain the existing good forest management measures, strengthen the scientific management and monitoring of forest resources, and prevent the reduction of forest area and the decline of forest quality.

[0117] Step 403: Determine the forest carbon sink potential level, and the specific steps include: According to the forest carbon sink sustainability index , the total soil carbon storage and the temperature change rate , calculate the forest carbon sink potential evaluation value , and the formula is:

[0118] where is the soil bulk density.

[0119] It should be noted that to obtain the soil bulk density , the soil porosity nl is directly measured by using a soil structure instrument, and then the soil bulk density is calculated , and the calculation formula is:

[0120] where is the soil particle density.

[0121] It should be noted that the formula comprehensively evaluates the forest carbon sink potential by correlating the forest carbon sink sustainability index , the total soil carbon storage , the temperature change rate and the soil bulk density . The higher the forest carbon sink sustainability index, the larger the total soil carbon storage, and the smaller the temperature change rate, the higher the evaluation value of the forest carbon sink potential.

[0122] The threshold values of the forest carbon sink potential evaluation value include the first threshold value of the forest carbon sink potential evaluation value and the second threshold value of the forest carbon sink potential evaluation value , and ; by using the LPJ-GUESS ecosystem process model, inputting the climate data, soil data, vegetation data, etc. of the study area, and running the model to obtain the simulation results of the forest carbon sink potential evaluation value. The Monte Carlo simulation method is used to analyze the probability distribution of the simulation results, and the 30% quantile of the simulation results is taken as the first threshold value of the forest carbon sink potential evaluation value; the 60% quantile of the simulation results is taken as the second threshold value of the forest carbon sink potential evaluation value.

[0123] When , it is judged that the forest carbon sink potential level is low, and forest management measure one is taken; forest management measure one is to replace the tree species with slow growth and low biomass with tree species with rapid growth and high biomass.

[0124] When , it is judged that the forest carbon sink potential level is medium, and forest management measure two is taken; forest management measure two is to reasonably prune the trees, remove dead branches and competing branches, and improve the shape and growth structure of the trees.

[0125] When , it is judged that the forest carbon sink potential level is high, and forest management measure three is taken; forest management measure three is to adopt ecological logging technology and strictly control the logging intensity and logging method.

[0126] Integrating steps 401 to 403: The calculation and grade judgment of the forest carbon sink sustainability index can clarify the sustainability level of forests in terms of carbon sinks, and the protection and restoration measures taken can specifically ensure the long-term stability of the forest carbon sink function. The calculation and grade judgment of the forest carbon sink potential assessment value help to fully explore the potential capacity of forests in carbon absorption and storage. Through corresponding forest management measures, the forest management model can be optimized and the forest carbon sink efficiency can be improved.

[0127] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution.

[0128] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0129] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application.

Claims

1. A big data prediction method for forest carbon sink potential, characterized in that: Including: Step 1: Collect the growth parameter data, soil data, meteorological data and forest resource data of the forest; Step 2: Calculate the tree growth status indicators based on the growth parameter data, soil data, and climate data ; Calculate the soil microbial biomass carbon content based on the soil data , and calculate the total soil carbon storage based on the soil microbial biomass carbon content and the soil data ; Calculating the vegetation coverage factor based on growth parameter data , calculating the vegetation coverage according to the vegetation coverage factor and forest resource data ; calculating the forest canopy density according to the vegetation coverage and forest resource data ; calculating the environmental impact index based on meteorological data ; Step 3: According to the tree growth status indicators , the total soil carbon storage , the forest canopy density and the environmental impact indicators , calculate the comprehensive indicator ; According to the comprehensive indicator , the soil data and the meteorological data, calculate the forest ecological activity index ; Step 4: Calculate the forest carbon sink sustainability index based on the forest ecological activity index , growth parameter data, and meteorological data; Preset the threshold of the forest carbon sink sustainability index, and compare the forest carbon sink sustainability index with the threshold of the forest carbon sink sustainability index. According to the result, judge the sustainability level of the forest and take corresponding protection and restoration measures; according to the forest carbon sink sustainability index , the total soil carbon storage and meteorological data, calculate the evaluation value of the forest carbon sink potential ; Preset the threshold value of the forest carbon sink potential assessment value, and compare the forest carbon sink potential assessment value with the threshold value of the forest carbon sink potential assessment value. Judge the forest carbon sink potential level according to the result, and take corresponding forest management measures.

2. The method for predicting big data of forest carbon sink potential according to claim 1, characterized in that: Method for calculating tree growth state index is as follows: The growth parameter data includes the mean diameter at breast height , the mean tree height , the mean crown width , the mean tree age and the mean tree branch angle ; Soil data includes the average soil water content ; Meteorological data includes the average wind speed ; According to the average diameter at breast height , the average tree height , the average crown width , the average tree age , the average tree branch angle , the average soil water content and the average wind speed , calculate the tree growth status index , and the formula used is: where e is the natural constant; is the mean slope.

3. A method for predicting big data of forest carbon sink potential according to claim 2, characterized in that: Method for calculating total soil carbon storage is as follows: The soil data also includes the soil microbial respiration frequency , the soil organic carbon content and the soil porosity ; Based on the soil microbial respiration frequency , calculate the soil microbial biomass carbon content , and the calculation formula is: Among them, is the conversion factor; is the empirical coefficient; Based on the soil microbial biomass carbon content , soil organic carbon content and soil porosity , calculate the total soil carbon storage , and the formula used is: Among them, is the microbial decomposition coefficient.

4. A method for predicting big data of forest carbon sink potential according to claim 3, characterized in that: Method for calculating forest canopy density is as follows: The growth parameter data further includes the vegetation growth density , the average vegetation growth height and the average vegetation growth diameter ; Forest resource data includes the total area of forest regions ; According to the vegetation growth density , the average vegetation growth height and the average vegetation growth diameter , calculate the vegetation coverage factor , and the calculation formula is: Among them, is the vegetation growth density of the i-th vegetation type; is the average vegetation growth height of the i-th vegetation type; is the average vegetation growth diameter of the i-th vegetation type; is the vegetation complexity coefficient of the i-th vegetation type; According to the vegetation coverage factor and the total area of the forest area , calculate the vegetation coverage , and the calculation formula is: Among them, is the area of the i-th vegetation type in the forest area, where i is the serial number of the vegetation type, taking values in [1, n]; n is the number of different vegetation types in the forest area, taking positive integer values; According to the vegetation coverage and the total area of the forest area , calculate the forest canopy density , and the formula is as follows: Among them, is the average crown area of trees in the forest.

5. A method for predicting big data of forest carbon sink potential according to claim 4, characterized in that: Method for calculating environmental impact indicators is as follows: The meteorological data also includes the annual average temperature , annual precipitation , accumulated temperature and temperature change rate ; According to the annual average temperature , annual precipitation , accumulated temperature and temperature change rate , calculate the environmental impact index , and the formula is as follows: Among them, is 's weight coefficient, with a value range of 0.2 to 0.4; is 's weight coefficient, with a value range of 0.1 to 0.3; is 's weight coefficient, with a value range of 0.3 to 0.5; is 's weight coefficient, with a value range of 0.2 to 0.4; and .

6. A method for predicting big data of forest carbon sink potential according to claim 5, characterized in that: Method for calculating comprehensive index is as follows: According to the tree growth status indicators , the total soil carbon storage , the forest canopy density and the environmental impact indicators , calculate the comprehensive indicator , and the specific formula is: Among them, is the weight coefficient of the tree growth status index , with a value range of 0.2 to 0.3; is the weight coefficient of the total soil carbon storage , with a value range of 0.2 to 0.4; is the weight coefficient of the forest canopy density , with a value range of 0.3 to 0.4; is the weight coefficient of the environmental impact index , with a value range of 0.1 to 0.3; and .

7. A method for predicting big data of forest carbon sink potential according to claim 6, characterized in that: Method for calculating forest ecological activity index is as follows: The soil data also includes the soil area ; According to the comprehensive index , soil area and temperature change rate , calculate the forest ecological activity index , and the formula is as follows: Among them, is the species richness; is the ecological value assessment value; is the leaf area index; is the moisture content of the forest.

8. A method for predicting big data of forest carbon sink potential according to claim 7, characterized in that: Method for calculating sustainable index of forest carbon sink is as follows: Based on the average tree age , the forest ecological activity index and annual precipitation , calculate the forest carbon sink sustainability index , and the formula is as follows: Among them, is the forest area growth rate; is the deforestation rate; is the forest degradation rate.

9. The method for predicting big data of forest carbon sink potential according to claim 8, characterized in that: The method for judging the sustainability level of the forest is: The threshold of the forest carbon sink sustainability index includes the first threshold of the forest carbon sink sustainability index and the second threshold of the forest carbon sink sustainability index , and ; When the sustainability level of the forest is judged to be low, protection and restoration measure one is taken; When it is determined that the sustainability level of the forest is intermediate, and protection and restoration measures II are taken; When the sustainability level of the forest is judged to be high, protection and restoration measures three are taken.

10. A method for predicting big data of forest carbon sink potential according to claim 9, characterized in that: The method for judging the forest carbon sink potential level is: According to the forest carbon sink sustainability index , the total soil carbon storage and the temperature change rate , calculate the evaluation value of the forest carbon sink potential . The formula is as follows: Among them, is the soil bulk density; The threshold of the forest carbon sink potential assessment value includes the first threshold of the forest carbon sink potential assessment value and the second threshold of the forest carbon sink potential assessment value , and ; When it is determined that the forest carbon sink potential level is low, forest management measure one is taken; When the forest carbon sink potential level is judged to be intermediate, forest management measure 2 is adopted; When it is determined that the forest carbon sink potential level is high, forest management measure three is taken.

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