Mangrove forest biomass carbon reserve estimation method and mangrove forest biomass carbon reserve estimation system based on different-speed growth inversion model

Abstract

The invention relates to the technical field of carbon reserve estimation, and discloses a mangrove forest biomass carbon reserve estimation method based on a different-speed growth inversion model, and the method comprises the following steps: 1, setting a quadrat region; 2, collecting aerial images, kandelia candel morphological data, kandelia candel destructive data and soil profile organic carbon density data; step 3, establishing an allotropical growth inversion model; processing the kandelia candel destructive sampling parameters to obtain a kandelia candel carbon conversion coefficient; performing image processing and visual interpretation on the aerial image of the research area to obtain the crown breadth coverage total area and the mud flat coverage area of the kandelia candel in the research area; 4, the overall kandelia candel carbon reserve is obtained by combining the kandelia candel morphological data, the total crown breadth coverage area of the kandelia candel, the different-speed growth inversion model and the kandelia candel carbon conversion coefficient; and combining the organic carbon density data of the soil profile and the mud flat coverage area to obtain the overall soil carbon reserve. Meanwhile, the invention further provides a mangrove forest biomass carbon reserve estimation system based on the different-speed growth inversion model.

Description

Technical Field

[0001] This invention relates to the field of carbon storage estimation technology, and in particular to a method and system for estimating mangrove biomass carbon storage based on an allometric growth inversion model. Background Technology

[0002] Traditional methods for assessing forest biomass mainly include logging, average tree method, and allometric growth models. While logging can directly measure tree dry weight and obtain biomass, its destructive impact on the ecosystem cannot be ignored. The average tree method is limited to forests with relatively uniform tree size, thus its applicability is limited. Allometric growth models, due to their non-destructive nature, have become the most commonly used method for biomass estimation today, but they are also limited by manual data collection, consuming significant human, material, and financial resources. With advancements in technology, remote sensing satellite technology has opened new avenues for forest resource surveys. For example, many scholars have used multispectral, hyperspectral, and high-resolution images, as well as synthetic aperture radar (SAR) data, to explore how to estimate tree biomass using remote sensing technology. However, problems remain regarding the completeness and quality of remote sensing data, high costs, and the complexity of processing techniques.

[0003] CN202311156020 discloses a method for estimating aboveground carbon storage of mangrove plants in the marine-terrestrial ecotone, comprising the following steps: acquiring survey data and multi-temporal satellite imagery data of the study area, preprocessing the survey data, filtering and preprocessing the multi-temporal satellite imagery data to obtain preprocessed survey data and preprocessed multi-temporal satellite imagery data; creating classification training samples based on the preprocessed survey data, using the preprocessed multi-temporal satellite imagery data as the classification basis data, and using a random forest machine learning algorithm to remotely identify the distribution range of mangroves to obtain the distribution range of mangroves in the study area; acquiring corresponding medium-to-high spatial resolution multispectral satellite imagery based on the distribution range of mangroves in the study area and combining it with the preprocessed survey data to construct an aboveground biomass inversion regression model for mangroves in the study area; obtaining the carbon conversion coefficient of mangrove aboveground biomass, and estimating the aboveground carbon storage of mangrove plants in the study area by combining the medium-to-high spatial resolution multispectral satellite imagery of the distribution range of mangroves in the study area and the aboveground biomass inversion regression model for mangroves in the study area to obtain the aboveground carbon storage of mangrove plants in the study area. This carbon storage assessment method achieves automatic and accurate identification of mangroves and coastal salt marshes through multi-temporal multispectral satellite remote sensing imagery and field survey data, improving the degree of automation. However, this method is costly and relatively complex to operate.

[0004] The technical problem that this invention aims to solve is: how to estimate the carbon storage of large-area, sparsely distributed, and clearly defined mangrove forests in a low-cost, efficient, and high-precision manner. Summary of the Invention

[0005] The main objective of this invention is to provide a method for estimating mangrove biomass carbon storage based on an allometric growth inversion model. This method utilizes visual interpretation and 3D software to extract the projected area of ​​the Kandelia candel canopy, and then combines this with the allometric growth equation to roughly predict the overall vegetation biomass distribution of a large-scale study area using limited field survey data. The carbon storage of the Yueqing mangrove forest on the northern edge of China's artificial forests is estimated by integrating the allometric growth equation, field plot data, UAV imagery, and carbon conversion coefficients. This method is low-cost, simple to operate, and highly accurate.

[0006] In addition, a system for estimating mangrove biomass carbon storage based on an allometric growth inversion model is also provided.

[0007] To achieve the above objectives, the technical solution adopted in this application is as follows:

[0008] A method for estimating mangrove biomass carbon storage based on an allometric growth inversion model includes the following steps:

[0009] Step 1: Establish quadrat areas within the study area;

[0010] Step 2: Collect aerial images of the study area, as well as morphological data, destructive data, and soil profile organic carbon density data of Kandelia candel within the sample plots;

[0011] Step 3: Using various Kandelia candel morphological data as inversion factors, establish an allometric growth inversion model and select the best allometric growth inversion model; process the destructive sampling parameters of Kandelia candel to obtain the Kandelia candel carbon conversion coefficient; after image processing and visual interpretation of aerial images of the study area, obtain the total canopy coverage area and tidal flat coverage area of ​​Kandelia candel in the study area.

[0012] Step 4: Combine the morphological data of Kandelia candel, the total canopy coverage area of ​​Kandelia candel, the allometric growth inversion model, and the carbon conversion coefficient of Kandelia candel to obtain the overall carbon storage of Kandelia candel; combine the soil profile organic carbon density data and the tidal flat coverage area to obtain the overall soil carbon storage.

[0013] Preferably, in step 2, the morphological data of Kandelia candel is the average branch diameter (D). a ), base diameter (D) b ), canopy diameter (D) c ), height (H), crown area (D) ca One or more combinations thereof;

[0014] The destructive data for Kandelia candel are: dry weight of each component, above-ground fresh weight, underground fresh weight, total fresh weight, above-ground dry weight, underground dry weight, and total dry weight.

[0015] The base diameter (D) bThis refers to the base diameter of the mangrove at a distance of 10cm from the bottom surface;

[0016] The dry weight of each component of the autumn eggplant is the dry weight of the leaves, tender branches, lignified branches, stems and roots of the autumn eggplant.

[0017] Preferably, in step 3, the method for establishing an allometric growth inversion model using Kandelia candel morphological data as an inversion factor is as follows:

[0018] Step 31: Establish multiple allometric growth inversion models, and use different Kandelia candel morphological data as inversion factors for different allometric growth inversion models;

[0019] Step 32: Employ the Akaike Information Criterion, Bayesian Information Criterion, Root Mean Square Error Information Criterion, and Coefficient of Determination R0. 2 The optimal allometric growth inversion model was obtained by screening based on information criteria.

[0020] The allometric growth inversion model includes the allometric growth equation between the inversion factor and AGB, the allometric growth equation between the inversion factor and BGB, and the allometric growth equation between the inversion factor and TB.

[0021] The allometric growth equation between the inversion factor and AGB is:

[0022] In(AGB) = a + b × In(x);

[0023] In(BGB) = c + d × In(x);

[0024] In(TB) = e + f × In(x);

[0025] x is the inversion factor; a, b, c, d, e, and f are all constants; AGB is the aboveground biomass of Kandelia candel, BGB is the underground biomass of Kandelia candel, and TB is the total biomass of Kandelia candel.

[0026] Preferably, the inversion factor is the average branch diameter (D). a ), base diameter (D) b ), canopy diameter (D) c D a 2 ×H、D b 2 ×H、D c 2 One or more combinations of ×H.

[0027] Preferably, step 32 specifically includes:

[0028] Step 321: Calculate whether the AIC / BIC of different allometric growth inversion models is less than a first preset value using the Akaike information criterion and the Bayesian information criterion. Then, proceed to step 322 using the allometric growth inversion model whose AIC / BIC value is less than the first preset value. AIC is the result calculated using the Akaike information criterion; BIC is the result calculated using the Bayesian information criterion.

[0029] Step 322: Use the root mean square error information criterion and the coefficient of determination R. 2 Information criterion calculation step 321: Select the allometric growth inversion models and determine the RMSE and R of each allometric growth inversion model. 2 According to RMSE and R 2 Select the best allometric growth inversion model.

[0030] Preferably, in step 2, the specific method for acquiring aerial images of the study area is as follows: under low wind speed and rainless environmental conditions, a consumer-grade drone is used to take photos of the study area at equal intervals to obtain aerial images; the low wind speed is defined as wind speed < 7.7 m / s.

[0031] Preferably, in step 3, the specific operation for processing the destructive sampling parameters of Kandelia candel to obtain the Kandelia candel carbon conversion coefficient is as follows: calculate the proportion of the dry weight of each component of Kandelia candel to the corresponding above-ground or below-ground dry weight, calculate the carbon content of each component of Kandelia candel using the potassium dichromate volumetric method, and obtain the Kandelia candel carbon conversion coefficient using the following formula:

[0032] Cex = Sum[Ads(B)%×w(C)];

[0033] Wherein, Cex is the carbon conversion coefficient of Kandelia candel; Ads(B)% is the percentage of the dry weight of each component of Kandelia candel to the corresponding above-ground or below-ground dry weight (%); w(C) is the carbon content of each component of Kandelia candel (%).

[0034] In step 3, after image processing and visual interpretation of the aerial images, the specific operation to obtain the total canopy coverage area and mudflat coverage area of ​​the Kandelia candel in the study area is as follows: by performing aerial triangulation analysis on the aerial images, a digital surface model (DSM) and a digital orthophoto (DOM) are obtained. A three-dimensional map is established based on the digital surface model (DSM) and the digital orthophoto (DOM). Researchers perform visual interpretation of the three-dimensional map to obtain the canopy coverage area and mudflat coverage area of ​​the Kandelia candel in the study area.

[0035] Preferably, in step 4, the specific operation of obtaining the overall carbon storage of Kandelia candel by combining morphological data, total canopy coverage area, allometric growth inversion model, and carbon conversion coefficient includes the following steps:

[0036] Step 41: Adjust the base diameter (D) bInputting the allometric growth inversion model yields the biomass of Kandelia candel, which is then combined with the corresponding canopy area (D). ca The biomass density was obtained; based on the biomass density and the canopy coverage area of ​​Kandelia candel, the aboveground biomass and underground biomass of Kandelia candel were obtained.

[0037] Step 42: Based on the aboveground biomass, underground biomass, and carbon conversion coefficient of Kandelia candel, obtain the overall carbon storage of Kandelia candel.

[0038] Preferably, in step 41, the formulas for calculating the aboveground biomass and belowground biomass of *Kandelia gigantea* are as follows:

[0039]

[0040]

[0041] Among them, S i Let S be the crown area of ​​the i-th mangrove. 总 The total area covered by the canopy of the Kandelia candel, D bi Let be the base diameter of the i-th mangrove;

[0042] The Kandelia candel mentioned refers to the Kandelia candel tree with a height between 0.5 and 2 meters;

[0043] The length and width of the sample plot area are both 5m.

[0044] In addition, a mangrove biomass carbon storage estimation system based on an allometric growth inversion model is also provided, including the following units:

[0045] Quadrat establishment unit: Used to establish quadrat areas within the study area;

[0046] Data acquisition unit: used to collect aerial images of the study area, as well as morphological data of Kandelia candel, data on the damage caused by Kandelia candel, and data on soil profile organic carbon density within the sample plots;

[0047] Data processing unit: used to use Kandelia candel morphological parameters as inversion factors to establish an allometric growth inversion model and select the best allometric growth inversion model; to process the destructive sampling parameters of Kandelia candel to obtain the carbon conversion coefficient of Kandelia candel; and to process and visually interpret the aerial images of the study area to obtain the total canopy coverage area and tidal flat coverage area of ​​Kandelia candel in the study area.

[0048] Carbon storage calculation unit: used to combine Kandelia candel morphological data, total canopy coverage area, allometric growth model, and Kandelia candel carbon conversion coefficient to obtain the overall Kandelia candel carbon storage; combined with soil profile organic carbon density data and tidal flat coverage area to obtain the overall soil carbon storage;

[0049] The area establishment unit, data acquisition unit, data processing unit, and carbon storage calculation unit are sequentially connected in communication.

[0050] Compared with existing technologies, this solution has the following advantages:

[0051] This approach uses an allometric growth inversion model combined with drone aerial photography to estimate biomass. It not only eliminates the need for extensive field surveys but also avoids errors caused by uneven vegetation distribution, insufficient quadrat distribution, and overall lack of representativeness, thereby reducing the workload of researchers and improving efficiency.

[0052] This innovative approach proposes using visual interpretation and 3D software to extract the projected area of ​​the Kandelia candel canopy, and then combining this with allometric growth equations to roughly predict the overall vegetation biomass distribution of a large-scale study area using limited field survey data. By integrating allometric growth equations, field plot data, UAV imagery, and carbon conversion coefficients to estimate mangrove carbon storage, this method is both cost-effective and highly efficient, providing a reference for monitoring large areas of sparsely distributed, well-defined mangroves. Attached Figure Description

[0053] Figure 1 This is a flowchart of the estimation of mangrove biomass carbon storage based on the allometric growth inversion model in Example 1;

[0054] Figure 2 This is a sampling image of the destructive logging of Kandelia candel in Example 1;

[0055] Figure 3 As in Example 1, D b AGB fitting effect diagram of mangrove Kandelia candel forest as inversion factor;

[0056] Figure 4 As in Example 1, D b The BGB fitting effect diagram of mangrove Kandelia candel forest as an inversion factor;

[0057] Figure 5 As in Example 1, D b The TB fitting effect diagram of mangrove forest as an inversion factor. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of this application implemented as described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0059] The experimental area selected for this application is one island, two islands, three islands, four islands, and five islands. This area is the northernmost artificial mangrove forest in China, covering an area of ​​approximately 170 hectares. Since 1957, the only mangrove species introduced into the forest stands is Kandelia candel. The drop in temperature and occasional extreme cold events (temperatures can drop as low as -5.5°C) make the growth environment of Kandelia candel in this area exceptionally harsh, resulting in the age of Kandelia candel forests being almost all less than 10 years, and Kandelia candel exhibiting typical small size (plant height H < 2.0 m). Therefore, this application takes two types of autumn vegetation as the research object: (1) Kandelia candel seedlings, with a plant height of 0.4–1.0 m; (2) Mature Kandelia candel forests, with an average plant height > 1.0 m.

[0060] Example 1

[0061] refer to Figure 1-5 A method for estimating mangrove biomass carbon storage based on an allometric growth inversion model includes the following steps:

[0062] Step 1: Establish quadrat areas within the study area;

[0063] 1. Study Area Plot Layout Method: A random sampling method was used to establish 48 high-density artificial mangrove vegetation monitoring plots (5m × 5m) within the intertidal zone of five mangrove planting areas in Yueqing Bay, Zhejiang Province (Island 1, Island 5, Island 3, Island 4, and Island 2). Mangrove vegetation elements were monitored and soil samples were collected. Twelve plots were established on Island 1, Island 5, and Island 4, where mangrove distribution areas were larger, while six plots were established on Island 2 and Island 3, where mangrove distribution areas were smaller. Field surveys were conducted within the established plots in the study area. High-precision Huace RTK equipment was used to accurately locate and measure the elevation at the four corners and center of each square plot.

[0064] Step 2: Collect aerial images of the study area, as well as morphological data, destructive data, and soil profile organic carbon density data of Kandelia candel within the sample plots;

[0065] 2.1 Morphological Data Collection of Kandelia candel: Morphological data of ten Kandelia candel plants in each quadrat were measured and recorded using tools such as vernier calipers and measuring tapes, including D... a D b D c H and D ca The measured data collection results included morphological data of 480 plants from the five islands.

[0066] 2.2 Destructive Data Collection of Kandelia candel: 30 representative Kandelia candel trees of varying ages within the sample plot were destructively felled and sampled. Figure 2As shown. Carefully selected whole plants, including the underground parts, were excavated, packaged, and immediately brought back to the laboratory for cleaning and natural air-drying. After air-drying, destructive data collection was performed on 30 samples, and the results were recorded.

[0067] Using standard botanical methods, 30 excavated Kandelia candel plants were divided into five parts using a chainsaw and pruning shears: leaves, tender shoots, lignified branches, stems, and roots. The leaves, tender shoots, lignified branches, and stems were considered the aboveground vegetation, while the roots were considered the underground vegetation. The fresh biomass of both the aboveground and underground parts of each Kandelia candel sample was then measured and recorded using an electronic balance and a 50 kg (0.2 kg accuracy) spring-loaded scale.

[0068] Thirty Kandelia candel samples were collected from each plant. The leaves, young shoots, lignified branches, stems, and roots were chopped into small pieces using pruning shears and mixed thoroughly. Then, 200–250 g of fresh sample was taken from each of the five parts of the sample and packaged. After packaging, the fresh weight of the sample was measured using a digital balance and the data was recorded. The sample was then dried in an oven at 105°C for 72 hours, and the dry weight was measured using a digital balance. Finally, based on the ratio of fresh weight to dry weight of each part of the sample, the aboveground, underground, and total biomass dry weights of the 30 Kandelia candel samples were estimated. The parameters of the 30 Kandelia candel samples are shown in Table 1-5.

[0069] Table 1. Parameters of a sample of Kandelia candel from a certain island

[0070]

[0071]

[0072] Table 2 Parameters of a certain Goto Kande sample

[0073] serial number 8 9 10 11 12 13 <![CDATA[D a (cm)]]> 1.57 2.02 1.77 2.54 2.72 2.00 <![CDATA[D b (cm)]]> 3.66 3.78 3.23 9.56 6.70 7.16 <![CDATA[D c (cm)]]> 52.00 50.00 42.00 87.00 72.00 77.00 H(cm) 66.00 96.00 65.00 136.00 110.00 106.00 Fresh weight on the ground (g) 607.79 540.02 313.69 2142.36 1376.08 1269.02 Fresh weight underground (g) 338.62 315.55 257.44 3420.76 697.66 864.57 Total fresh weight (g) 946.41 855.57 571.13 5563.12 2073.74 2133.59 Dry weight on the ground (g) 190.40 182.11 98.06 713.69 482.22 421.93 Dry weight of underground soil (g) 74.03 65.31 60.57 797.14 169.80 180.55 Total dry weight (g) 264.43 247.42 158.63 1510.83 652.02 602.48

[0074] Table 3. Parameters of a certain Mishima mangrove sample

[0075] serial number 14 15 16 17 18 19 <![CDATA[D a (cm)]]> 2.47 1.24 0.93 1.19 1.28 1.74 <![CDATA[D b (cm)]]> 4.75 2.47 2.19 1.19 1.28 1.74 <![CDATA[D c (cm)]]> 32.00 29.00 25.00 7.00 20.00 21.00 H(cm) 105.00 87.00 73.50 53.00 78.00 79.00 Fresh weight on the ground (g) 564.57 188.08 199.36 25.91 49.04 86.33 Fresh weight underground (g) 345.54 138.55 127.20 24.75 53.16 89.80 Total fresh weight (g) 910.11 326.63 326.56 50.66 102.20 176.13 Dry weight on the ground (g) 137.12 56.50 65.22 3.97 8.03 13.40 Dry weight of underground soil (g) 67.69 26.27 25.35 3.33 38.37 13.00 Total dry weight (g) 204.81 82.77 90.57 7.30 46.40 26.40

[0076] Table 4. Parameters of a certain Shijima mangrove sample

[0077]

[0078]

[0079] Table 5. Parameters of a certain island's Kandelia candel sample

[0080] serial number 25 26 27 28 29 30 <![CDATA[D a (cm)]]> 3.10 3.04 2.71 2.35 2.20 5.00 <![CDATA[D b (cm)]]> 11.81 11.12 5.63 6.78 5.41 7.45 <![CDATA[D c (cm)]]> 68.00 64.00 86.00 43.00 32.00 65.00 H(cm) 133.00 133.00 167.00 99.00 99.00 110.00 Fresh weight on the ground (g) 3239.80 2072.01 2648.54 7169.17 1964.82 1532.02 Fresh weight underground (g) 9591.80 8157.10 665.66 855.67 693.74 866.79 Total fresh weight (g) 12831.60 10229.11 3314.20 8024.84 2658.56 2398.81 Dry weight on the ground (g) 797.80 518.28 697.21 838.06 326.17 545.20 Dry weight of underground soil (g) 673.28 568.57 212.28 256.37 228.97 271.22 Total dry weight (g) 1471.08 1086.85 909.49 1094.43 555.14 816.42

[0081] 2.3 Soil Profile Organic Carbon Density Data Collection: Soil samples were collected from the mangrove areas within the designated quadrats in the study area. Topsoil was removed using shovels, and then custom-made stainless steel ring-blade soil samplers were used to collect soil samples from the top 0–10 cm, 10–20 cm, 20–30 cm, 30–40 cm, 40–50 cm, 50–70 cm, and 70–100 cm depths in each monitoring area. Finally, a portion of the soil samples from each layer was placed in sealed PVC soil bulk density sampling tubes, and the remaining soil samples were placed in self-sealing bags and taken back to the laboratory. A total of 336 bags of soil samples and 336 soil bulk density tube samples from different soil layers were collected from the five major areas.

[0082] The collected soil samples and density tubes were placed in an open area to air dry. After the samples were dry, the soil samples in the resealable bags were pulverized into powder using a mixer and passed through a 2mm soil sieve before being repackaged. The height and diameter of each density tube were measured and recorded using vernier calipers, and the volume of the density tube was calculated (cm³). 3 Weigh and record the dry weight (g) of the soil column dried in each bulk density tube using a digital balance; calculate the bulk density (g / cm³) of different soil layers in different plots based on the volume of the bulk density tube and the dry weight of the soil column. 3 ).

[0083] The total soil carbon (TC) and soil organic carbon (SOC) content of each soil layer were determined and recorded using the potassium dichromate oxidation external heating method after pulverization of soil samples. Finally, the organic carbon density (T, MgC·ha) per unit area at a certain depth in the soil profile was calculated. -1 The calculation formula is as follows:

[0084] T=∑D i ×M i ×d i

[0085] Among them, D i The bulk density of the i-th soil layer (g / cm³) 3 M i The organic carbon content (g / kg) of the i-th soil layer, d i Let be the thickness of the i-th soil layer (m). The final average soil bulk density and organic carbon content of different regions within each study area were used to obtain the organic carbon storage per unit area at a certain depth within the study area. The soil survey data parameters are shown in Table 6.

[0086] Table 6 Soil Measured Survey Data Parameters

[0087]

[0088]

[0089] 2.4 Aerial Image Acquisition: A DJI Phantom 4 Pro RTK quadcopter was used for the flight mission. This study employed DJI GSPRO software to meticulously design the flight path using a cross-grid mode, ensuring the effective construction of the 3D model. The following settings were implemented in the software: flight altitude was set to 73m; flight speed to 5.8m / s; ground image resolution to 2.00cm / pixel; to ensure the accuracy of the drone's point cloud, the mission set the lateral overlap rate to 70% and the longitudinal overlap rate to 80%; to ensure the accuracy of the point cloud altitude data, the gimbal pitch angle was set to 75°; and the photo-taking mode was set to equidistant intervals during the flight mission. The survey was conducted only under suitable weather conditions, including low wind speed (<7.7m / s), no rain, and low tide. This study completed aerial image acquisition of mangrove vegetation areas in five major regions of Yueqing Bay: one island, three islands, four islands, two islands, and five islands. The total flight area reached 356.00ha, and 6928 valid images (5472×3648 pixels) were obtained.

[0090] Step 3: Use the morphological data of Kandelia candel as an inversion factor to establish an allometric growth inversion model; process the destructive sampling parameters of Kandelia candel to obtain the carbon conversion coefficient of Kandelia candel; after image processing and visual interpretation of the aerial images of the study area, obtain the total canopy coverage area and tidal flat coverage area of ​​Kandelia candel in the study area.

[0091] 3.1 Establishment of the allometric growth inversion model:

[0092] 3.11 SPSS Statistics 27.0 statistical analysis software was used to perform D-scan analysis on 30 Kandelia candel samples. b Data calculation and linear regression modeling of above-ground dry weight, below-ground dry weight, and total dry weight. D b As inversion factors, an allometric growth inversion model was established to estimate the aboveground biomass (ABG), belowground biomass (BGB), and total biomass (TB) of *Kandelia candel* in an artificial mangrove forest. The general formula for the allometric growth equation is: In(y) = a + b × In(x), where y is the dry weight and x is the inversion factor. D from 30 *Kandelia candel* samples... b Substituting the data for aboveground dry weight, underground dry weight, and total dry weight into the allometric growth equation, and fitting them with linear regression, we obtain the corresponding values ​​of a and b, thus obtaining the allometric growth equations for ABG, BGB, and TB, respectively.

[0093] In(AGB) = 2.489 + 1.890 × In(D) b );

[0094] In(BGB) = 1.737 + 1.871 × In(D) b );

[0095] In(TB) = 2.978 + 1.842 × In(D) b );

[0096] With D b The fitting effects of ABG, BGB, and TB, used as inversion factors, for artificial mangrove forests are as follows: Figure 3-5 As shown.

[0097] 3.12 SPSS Statistics 27.0 statistical analysis software was used to perform D... a Data calculation and linear regression modeling of above-ground dry weight, below-ground dry weight, and total dry weight. D a As inversion factors, an allometric growth inversion model was established to estimate the aboveground biomass (ABG), belowground biomass (BGB), and total biomass (TB) of *Kandelia candel* in an artificial mangrove forest. The general formula for the allometric growth equation is: In(y) = a + b × In(x), where y is the dry weight and x is the inversion factor. D from 30 *Kandelia candel* samples... a Substituting the data for aboveground dry weight, underground dry weight, and total dry weight into the allometric growth equation, and fitting them with linear regression, we obtain the corresponding values ​​of a and b, thus obtaining the allometric growth equations for ABG, BGB, and TB, respectively.

[0098] In(AGB) = 3.570 + 2.267 × In(D) a );

[0099] In(BGB) = 2.753 + 2.335 × In(D) a );

[0100] In(TB) = 4.007 + 2.250 × In(D) a );

[0101] 3.13 SPSS Statistics 27.0 statistical analysis software was used to perform D-scan analysis on 30 Kandelia candel samples. c Data calculation and linear regression modeling of above-ground dry weight, below-ground dry weight, and total dry weight. D c As inversion factors, an allometric growth inversion model was established to estimate the aboveground biomass (ABG), belowground biomass (BGB), and total biomass (TB) of *Kandelia candel* in an artificial mangrove forest. The general formula for the allometric growth equation is: In(y) = a + b × In(x), where y is the dry weight and x is the inversion factor. D from 30 *Kandelia candel* samples... c Substituting the data for aboveground dry weight, underground dry weight, and total dry weight into the allometric growth equation, and fitting them with linear regression, we obtain the corresponding values ​​of a and b, thus obtaining the allometric growth equations for ABG, BGB, and TB, respectively.

[0102] In(AGB) = -2.167 + 1.942 × In(D) c );

[0103] In(BGB) = -2.406 + 1.795 × In(D) c );

[0104] In(TB) = -1.454 + 1.863 × In(D) c );

[0105] 3.14 SPSS Statistics 27.0 statistical analysis software was used to perform D-scan analysis on 30 Kandelia candel samples. a 2 Data calculation and linear regression modeling of ×H, aboveground dry weight, underground dry weight, and total dry weight. D a 2 Using ×H as the inversion factor, an allometric growth inversion model was established to estimate the aboveground biomass (ABG), belowground biomass (BGB), and total biomass (TB) of *Kandelia candel* in an artificial mangrove forest. The general formula for the allometric growth equation is: In(y)=a+b×In(x), where y is dry weight and x is the inversion factor. D from 30 *Kandelia candel* samples... a 2 Substituting the data for ×H, aboveground dry weight, underground dry weight, and total dry weight into the allometric growth equation, and fitting them through linear regression, we obtain the corresponding values ​​of a and b, thus obtaining the allometric growth equations for ABG, BGB, and TB, respectively:

[0106] In(AGB) = 0.003 + 0.882 × In(D) a 2 ×H);

[0107] In(BGB) = -1.076 + 0.936 × In(D) a 2 ×H);

[0108] In(TB) = 0.378 + 0.891 × In(D) a 2 ×H);

[0109] 3.15 SPSS Statistics 27.0 was used for statistical analysis of 30 Kandelia candel samples. b 2 Data calculation and linear regression modeling of ×H, aboveground dry weight, underground dry weight, and total dry weight. D b 2Using ×H as the inversion factor, an allometric growth inversion model was established to estimate the aboveground biomass (ABG), belowground biomass (BGB), and total biomass (TB) of *Kandelia candel* in an artificial mangrove forest. The general formula for the allometric growth equation is: In(y)=a+b×In(x), where y is dry weight and x is the inversion factor. D from 30 *Kandelia candel* samples... b 2 Substituting the data for ×H, aboveground dry weight, underground dry weight, and total dry weight into the allometric growth equation, and fitting them through linear regression, we obtain the corresponding values ​​of a and b, thus obtaining the allometric growth equations for ABG, BGB, and TB, respectively:

[0110] In(AGB) = 0.110 + 0.692 × In(D) b 2 ×H);

[0111] In(BGB) = -0.741 + 0.702 × In(D) b 2 ×H);

[0112] In(TB) = 0.594 + 0.683 × In(D) b 2 ×H);

[0113] 3.16 SPSS Statistics 27.0 statistical analysis software was used to perform D-scan analysis on 30 Kandelia candel samples. c 2 Data calculation and linear regression modeling of ×H, aboveground dry weight, underground dry weight, and total dry weight. D c 2 Using ×H as the inversion factor, an allometric growth inversion model was established to estimate the aboveground biomass (ABG), belowground biomass (BGB), and total biomass (TB) of *Kandelia candel* in an artificial mangrove forest. The general formula for the allometric growth equation is: In(y)=a+b×In(x), where y is dry weight and x is the inversion factor. D from 30 *Kandelia candel* samples... c 2 Substituting the data for ×H, aboveground dry weight, underground dry weight, and total dry weight into the allometric growth equation, and fitting them through linear regression, we obtain the corresponding values ​​of a and b, thus obtaining the allometric growth equations for ABG, BGB, and TB, respectively:

[0114] In(AGB) = -3.565 + 0.726 × In(D) c 2 ×H);

[0115] In(BGB) = -4.045 + 0.701 × In(D) c 2 ×H);

[0116] In(TB) = -2.945 + 0.710 × In(D) c 2 ×H);

[0117] 3.2 Screening of Allometric Growth Inversion Models:

[0118] 3.21 The AIC / BIC ratio of different allometric growth inversion models is calculated using the Akaike information criterion and the Bayesian information criterion to determine if it is less than a first preset value. The allometric growth inversion model with an AIC / BIC value less than the first preset value is used in step 3.22. AIC is the result calculated using the Akaike information criterion; BIC is the result calculated using the Bayesian information criterion. The first preset value can be adjusted according to actual operation; in this embodiment, the first preset value is 10. The formulas for AIC and BIC are as follows:

[0119]

[0120]

[0121] Among them, y i The actual value of the sample; y l is the predicted value of the sample; n is the number of independent variables; p is the number of inversion factors.

[0122] 3.22 The root mean square error information criterion and the coefficient of determination R are adopted. 2 Information criteria are used to calculate allometric growth inversion models with AIC / BIC values ​​less than 10, and the RMSE and R-values ​​of each allometric growth inversion model are evaluated. 2 According to RMSE and R 2 To select the optimal allometric growth inversion model, the smaller the RMSE, the better the R². 2 Higher RMSE and R value indicate higher prediction accuracy. 2 The formulas are as follows:

[0123]

[0124]

[0125] in, This is the average of the actual values.

[0126] The specific screening results are shown in Table 7:

[0127] Table 7 Results of Allometric Growth Model Screening

[0128]

[0129]

[0130] According to Table 7, in this embodiment, the optimal allometric growth model is based on D... b Allometric growth model as inversion factor.

[0131] 3.3 Calculation of the carbon conversion coefficient of Kandelia candel: Based on the data obtained from the excavation and measurement of 30 Kandelia candel samples, the biomass proportion of each organ component in the artificial Kandelia candel community was obtained.

[0132] To estimate the ratio of aboveground and belowground biomass for each component, the dry weight of each component should be compared with the total dry weight of the aboveground or belowground portion, as shown in the following formula:

[0133]

[0134] In the formula: Ads(B) represents the percentage of aboveground or belowground biomass of each component (%); m b Let m be the dry weight of each component (g); m is the total dry weight of the aboveground parts (g). Substituting these values ​​into the formula, we find that the stem of the Kandelia candel accounts for 26.10% of its aboveground biomass; the leaves account for 18.16%; the branches account for 43.38%; and the young shoots account for 12.34%. The root of the Kandelia candel accounts for 100% of its underground biomass.

[0135] Data from the dissection and processing of 30 Kandelia candel samples were used for experimental analysis of carbon content. The potassium dichromate titration method was used to determine the organic carbon content. The formula used is as follows:

[0136]

[0137] Wherein, w(C): carbon content (%) of each component of the eggplant; v0: volume of FeSO4 standard solution consumed when titrating the blank solution (ml); v: volume of FeSO4 standard solution consumed when titrating the sample solution (mL); c: concentration of FeSO4 standard solution (mol / L); M: molar mass of 1 / 4 carbon atom (0.003 kg / mol); m: mass of the sample (g).

[0138] The results showed that the part of Kandelia candel with the highest carbon content in the study area was the stem, with a carbon content of approximately 41.99%; the part with the lowest carbon content was the root, with a carbon content of approximately 36.17%; the parts with intermediate carbon content were the leaves (approximately 38.46%), the branches (approximately 41.86%), and the young branches (approximately 40.52%).

[0139] Using the data obtained above, the carbon conversion coefficient of the artificial Kandelia candel forest vegetation in Yueqing Bay was calculated. The carbon conversion coefficient is the ratio of the biomass of each vegetation component to the total aboveground or belowground biomass multiplied by the sum of the carbon contents of each component, as shown in the following formula:

[0140] Cex = Sum[Ads(B)%×w(C)];

[0141] Wherein, Cex is the carbon conversion coefficient of Kandelia candel; Ads(B)% is the percentage of the dry weight of each component of Kandelia candel to the corresponding above-ground or below-ground dry weight (%); w(C) is the carbon content of each component of Kandelia candel (%).

[0142] The final calculations showed that the carbon conversion coefficient of the aboveground part of the artificial Kandelia candel forest in Yueqing Bay was 0.4110, and the carbon conversion coefficient of the underground part was 0.3617.

[0143] 3.4 Aerial Image Processing: Aerial triangulation was performed on the aerial images using Tsinghua Shanwei EPS 6.0, a professional 3D mapping software, to generate a Digital Surface Model (DSM) and a Digital Orthophoto Map (DOM) of the study area. A 3D map of the study area was then created using the DSM and DOM. A mangrove researcher with image processing experience then visually interpreted and identified all individual artificial Kandelia candel trees and mangrove tidal flats within the study area. The surface volume tool in the software was used to extract the crown coverage area and tidal flat coverage area data for all individual Kandelia candel trees within the study area, as shown in Table 7.

[0144] Table 8 Coverage Area Parameters

[0145] area Crown coverage area (ha) Tidal flat coverage area (ha) A certain five islands 4.14 10.89 Two islands 1.71 1.65 Four islands 2.70 7.20 A certain three islands 0.61 2.65 a certain island 3.75 8.48 total 12.91 30.86

[0146] Step 4: Combine the morphological data of Kandelia candel, the total canopy coverage area of ​​Kandelia candel, the allometric growth inversion model, and the carbon conversion coefficient of Kandelia candel to obtain the overall carbon storage of Kandelia candel; combine the soil profile organic carbon density data and the tidal flat coverage area to obtain the overall soil carbon storage.

[0147] 4.1 Calculation of Overall Kandelia candel Carbon Storage: Overall Kandelia candel carbon storage includes aboveground and underground Kandelia candel carbon storage. First, the AGB and BGB of each of the 480 Kandelia candel trees surveyed in the quadrat were estimated using the allometric growth inversion model. The allometric growth equation In(y)=a+b×In(x) can be transformed into y=e a ×x b Where e is the base of the natural logarithm, which facilitates the calculation of Kandelia candel biomass. The results are then summed and divided by the cumulative value of the canopy coverage of individual plants to obtain the biomass density of Kandelia candel under the canopy area distribution in the study area. Finally, the total biomass of each part of the artificial Kandelia candel forest in the study area is obtained by extracting the total canopy area of ​​the entire Kandelia candel in the study area using UAV remote sensing technology. The specific calculation formula is as follows:

[0148]

[0149]

[0150] Among them, W AGBW represents aboveground biomass. BGB underground biomass; S i Let S be the crown area of ​​the i-th mangrove. 总 D represents the total area covered by the canopy of the Kandelia candel; bi Let be the base diameter of the i-th mangrove.

[0151] 4.2 The carbon storage of the artificial Kandelia candel forest in Yueqing Bay is divided into aboveground and underground parts. The aboveground carbon storage is the carbon storage of the aboveground part of the Kandelia candel vegetation, and the underground carbon storage is the sum of the carbon storage of the underground part of the Kandelia candel vegetation and the soil carbon storage.

[0152] The formula for estimating the aboveground carbon storage of artificial Kandelia candel forests in Yueqing Bay is as follows:

[0153] C AG =W AGB ×C ex

[0154] The formula for estimating the underground carbon storage of the artificial Kandelia candel forest in Yueqing Bay is as follows:

[0155] C BG =(W BGB ×C ex )+(T×sum aom )

[0156] Among them, C AG C represents the total aboveground carbon storage. BG W represents the total underground carbon storage. AGB For the aboveground biomass of Kandelia candel, W BGB For the underground biomass of Kandelia candel, C ex Here, T represents the corresponding aboveground or belowground Kandelia candel carbon conversion coefficient, and T is the organic carbon density (T, MgC·ha) per unit area at a certain depth in the soil profile. -1 );sum aom This refers to the area covered by tidal flats.

[0157] Example 2

[0158] A mangrove biomass carbon storage estimation system based on an allometric growth inversion model includes the following units:

[0159] Quadrat establishment unit: Used to establish quadrat areas within the study area;

[0160] Data acquisition unit: used to collect aerial images of the study area, as well as morphological data of Kandelia candel, data on the damage caused by Kandelia candel, and data on soil profile organic carbon density within the sample plots;

[0161] Data processing unit: used to use Kandelia candel morphological parameters as inversion factors to establish an allometric growth inversion model and select the best allometric growth inversion model; to process the destructive sampling parameters of Kandelia candel to obtain the carbon conversion coefficient of Kandelia candel; and to process and visually interpret the aerial images of the study area to obtain the total canopy coverage area and tidal flat coverage area of ​​Kandelia candel in the study area.

[0162] Carbon storage calculation unit: used to combine Kandelia candel morphological data, total canopy coverage area, allometric growth model, and Kandelia candel carbon conversion coefficient to obtain the overall Kandelia candel carbon storage; combined with soil profile organic carbon density data and tidal flat coverage area to obtain the overall soil carbon storage;

[0163] The area establishment unit, data acquisition unit, data processing unit, and carbon storage calculation unit are sequentially connected in communication.

[0164] The specific workflow of the carbon storage estimation system is as follows: The quadrat establishment unit determines the size and number of quadrat areas and sends the specific data to the data acquisition unit. The data acquisition unit collects morphological data of Kandelia candel, data on Kandelia candel damage, soil profile organic carbon density data, and aerial images of the study area within the quadrat area and sends the data to the data processing unit. The data processing unit processes the data, establishes multiple allometric growth inversion models, selects the best allometric growth inversion model, Kandelia candel carbon conversion coefficient, total canopy coverage area of ​​Kandelia candel, and tidal flat coverage area, and sends them to the carbon storage calculation unit to calculate the overall Kandelia candel carbon storage and the overall soil carbon storage.

[0165] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for estimating mangrove biomass carbon stock based on an allometric inversion model, characterized in that: The following steps are involved: Step 1: Set up sample plots in the study area; Step 2: Collect aerial images of the study area, as well as data on the morphology of Kandelia candel, data on its destructiveness, and data on organic carbon density in soil profiles within the sample area; Step 3: Using various Kandelia candel morphological data as inversion factors, an allometric inversion model was established, and the best allometric inversion model was screened; the Kandelia candel destructive sampling parameters were processed to obtain the Kandelia candel carbon conversion coefficient; after image processing and visual interpretation of the aerial images of the study area, the total canopy coverage area and tidal flat coverage area of ​​Kandelia candel in the study area were obtained; Step 4: Combine the morphological data of Kandelia ovata, the total area of ​​Kandelia ovata canopy coverage, the allometric inversion model, and the Kandelia ovata carbon conversion coefficient to obtain the overall Kandelia ovata carbon storage; combine the soil profile organic carbon density data and the tidal flat coverage area to obtain the overall soil carbon storage.

2. The method for estimating mangrove biomass carbon stock based on the allometric inversion model according to claim 1 is characterized in that: In step 2, the morphological data of Kandelia candel is the average branch diameter (D a ), base diameter (D b ), canopy diameter (D c ), height (H), crown area (D ca ) or one or more combinations thereof; The destructive data of Kandelia candel include dry weight of Kandelia candel components, above-ground fresh weight, underground fresh weight, total fresh weight, above-ground dry weight, underground dry weight, and total dry weight; The base diameter (D b ) means the base diameter of Kandelia candel at 10 cm from the bottom; The dry weight of each component of Kandelia candel is the dry weight of leaves, twigs, lignified branches, stems and roots of Kandelia candel.

3. The method for estimating mangrove biomass carbon stock based on the allometric inversion model according to claim 2 is characterized in that: In step 3, a method of using a variety of Kandelia candel morphological data as inversion factors to establish an allometric inversion model and screen out the best allometric inversion model is as follows: Step 31: Establish multiple allometric inversion models, where different allometric inversion models use different morphological data of Kandelia candel as inversion factors; Step 32: Use Akaike information criterion, Bayesian information criterion, root mean square error information criterion, and determination coefficient R 2 The best allometric inversion model was obtained by information criterion screening; The allometric inversion model includes an allometric equation between the inversion factor and AGB, an allometric equation between the inversion factor and BGB, and an allometric equation between the inversion factor and TB; The allometric equation between the inversion factor and AGB is: In(AGB)=a+b×In(x); In(BGB)=c+d×In(x); In(TB)=e+f×In(x); x is the inversion factor; a, b, c, d, e, and f are all constants; AGB is the aboveground biomass of Kandelia ovata, BGB is the underground biomass of Kandelia ovata, and TB is the total biomass of Kandelia ovata.

4. The method for estimating mangrove biomass carbon stock based on the allometric inversion model according to claim 3 is characterized in that: The inversion factor is the average branch diameter (D a ), base diameter (D b ), canopy diameter (D c )、D a 2 ×H、D b 2 ×H、D c 2 ×H or one or more combinations.

5. The method for estimating mangrove biomass carbon stock based on the allometric inversion model according to claim 3 is characterized in that: The step 32 is specifically as follows: Step 321: using the Akaike information criterion and the Bayesian information criterion to calculate whether the AIC / BIC of different allometric inversion models is less than a first preset value, and using the allometric inversion model whose AIC / BIC value is less than the first preset value to perform step 322; AIC is the result calculated by the Akaike information criterion; BIC is the result calculated by the Bayesian information criterion; Step 322: Using the root mean square error information criterion and the coefficient of determination R 2 The information criterion calculation step 321 selects the allometric inversion models obtained, and determines the RMSE and R of each allometric inversion model. 2 ; According to RMSE and R 2 Screening for the best allometric inversion model.

6. The method for estimating mangrove biomass carbon stock based on the allometric inversion model according to claim 1, characterized in that: In step 2, the specific method for collecting aerial images of the study area is: under low wind speed and rainless environmental conditions, a consumer-grade drone is used to take pictures of the study area at equal intervals to obtain aerial images; the low wind speed is a wind speed of less than 7.7 m / s.

7. The method for estimating mangrove biomass carbon stock based on the allometric inversion model according to claim 2, characterized in that: In step 3, the destructive sampling parameters of Kandelia candel are processed to obtain the specific operation of the Kandelia candel carbon conversion coefficient by calculating the proportion of the dry weight of each Kandelia candel component to the corresponding above-ground or underground dry weight, calculating the carbon content of each Kandelia candel component by potassium dichromate volumetric method, and obtaining the Kandelia candel carbon conversion coefficient by the following formula: Cex = Sum[Ads(B)%×w(C)]; Wherein, Cex is the carbon conversion coefficient of Kandelia candel; Ads(B)% is the proportion of the dry weight of each Kandelia candel component to the corresponding above-ground or underground dry weight (%); w(C) is the carbon content of each Kandelia candel component (%); In the step 3, after image processing and visual interpretation of the aerial images, the specific operations of obtaining the total crown coverage area and the tidal flat coverage area of ​​the Kandelia ovata in the study area are as follows: performing aerial triangulation on the aerial images to obtain a digital surface model (DSM) and a digital orthophoto (DOM), establishing a three-dimensional map based on the digital surface model (DSM) and the digital orthophoto (DOM), and the researchers visually interpreting the three-dimensional map to obtain the crown coverage area and the tidal flat coverage area of ​​the Kandelia ovata in the study area.

8. The method for estimating mangrove biomass carbon stock based on the allometric inversion model according to claim 2, characterized in that: In step 4, the specific operation of obtaining the overall Kandelia candel carbon storage by combining Kandelia candel morphological data, the total area covered by the canopy of Kandelia candel, the allometric inversion model, and the Kandelia candel carbon conversion coefficient includes the following steps: Step 41: Set the base diameter (D b ) was input into the allometric inversion model to obtain the biomass of Kandelia ovata, and then combined with the biomass of Kandelia ovata and the corresponding canopy area (D ca ), and obtain the biomass density; according to the biomass density and the canopy coverage area of ​​​​the autumn candel, obtain the aboveground biomass of the autumn candel and the underground biomass of the autumn candel; Step 42: Obtain the overall Kandelia candel carbon storage according to the Kandelia candel aboveground biomass, Kandelia candel underground biomass and Kandelia candel carbon conversion coefficient.

9. The method for estimating mangrove biomass carbon stock based on the allometric inversion model according to claim 8, characterized in that: In step 41, the calculation formulas for the aboveground biomass and underground biomass of Kandelia candel are: Among them, S i is the crown area of ​​the i-th Kandelia candel, S 总 is the total canopy coverage area of ​​Kandelia ovata, D bi is the base diameter of the i-th Kandelia candel; The Kandelia candel is a Kandelia candel with a tree height between 0.5 and 2 m; The length and width of the sample area are both 5m.

10. A mangrove biomass carbon stock estimation system based on allometric inversion model, characterized in that: The following units are included: Sample plot establishment unit: used to establish sample plot areas in the study area; Data collection unit: used to collect aerial images of the study area, as well as data on the morphology of Kandelia candel, data on its destructiveness, and data on organic carbon density in soil profiles in the sample area; Data processing unit: used to establish an allometric inversion model by taking the morphological parameters of Kandelia candel as inversion factors, and screen out the best allometric inversion model; process the destructive sampling parameters of Kandelia candel to obtain the Kandelia candel carbon conversion coefficient; perform image processing and visual interpretation on the aerial images of the study area to obtain the total canopy coverage area and tidal flat coverage area of ​​Kandelia candel in the study area; Carbon storage calculation unit: used to combine the morphological data of Kandelia candel, the total area of ​​candel canopy coverage, the allometric growth model, and the carbon conversion coefficient of Kandelia candel to obtain the overall Kandelia candel carbon storage; combined with the soil profile organic carbon density data and the tidal flat coverage area, to obtain the overall soil carbon storage; The area establishment unit, the data collection unit, the data processing unit, and the carbon storage calculation unit are communicatively connected in sequence.

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