Accounting method for removable carbon sink amount of macroalgae

Abstract

The invention provides a removable carbon sink amount accounting method for macroalgae. The method comprises the following steps: S1, extracting an algae culture area of a culture area from a remote sensing image through a classification extraction method; s2, the unmanned aerial vehicle is used for collecting sample points of the breeding area, and the breeding area of the sample points is counted; s3, comparing the areas obtained in the step S2 with the area obtained in the step S1 to obtain a correction coefficient of the remote sensing image area of the seaweed culture area; s4, summarizing the relationship between the corrected seaweed culture area and biomass in combination with unmanned aerial vehicle image analysis and a field investigation result to obtain culture biomass; and S5, calculating the removable carbon sink amount of the seaweed according to the following formula: the removable carbon sink amount = culture biomass * dry weight rate * carbon content. According to the method, the removable carbon sink amount of the macroalgae can be reflected, calculated, corrected and calculated practically in the determined sea area, and the method has an important guiding effect on removable carbon sink calculation of vast breeding enterprises and collective units.

Description

Technical Field

[0001] The invention relates to the field of removable carbon sink calculation, and in particular to a method for calculating the removable carbon sink of large seaweed. Background Art

[0002] Large seaweed has a long history of development. The seaweed industry was established and developed in the 1950s. After continuous recognition and development, seaweed has become a familiar product around people. In addition to its important economic value, large seaweed also has an ecological value that cannot be ignored. Cultivating large seaweed can optimize the structure of the marine ecosystem, participate in the marine carbon cycle, delay eutrophication of the sea area, control red tides, and participate in clean production of marine aquaculture. Cultivating large seaweed can absorb a large amount of carbon dioxide, which is an important component of marine blue carbon and plays an important role in increasing ocean carbon sinks. Large seaweed has high primary productivity and large aquaculture scale, which makes it a research object for negative emissions in the ocean. Marine aquaculture is an important production activity in my country's coastal waters. It can continuously promote the realization of the United Nations Sustainable Development Goals (SDGs) in terms of environment, biodiversity and climate goals. This is also an unprecedented nature-based solution. Farmed large seaweed can grow and accumulate a large amount of organic matter through photosynthesis, and the removable part is a huge amount of organic carbon. The cultivation of large seaweed does absorb a large amount of carbon dioxide, and this part of the carbon sink and the carbon source produced are both measurable. When considering the carbon sink value of large seaweed, the amount of removable organic carbon should also be considered. At present, farmed seaweed has not become an officially recognized blue carbon ecosystem, resulting in great difficulties in large-scale cultivation and promotion, so it is particularly important to clarify the amount of removable carbon sinks generated during its cultivation process.

[0003] However, there is basically no accounting method for large seaweed on the market. There is only the current industry standard "Marine Carbon Sink Accounting Method" (HY / T 0349-2022) for reference. The accounting method uses fishery statistics, which is difficult to calculate for a region or a company. Therefore, a method for calculating the removable carbon sink of large seaweed is urgently needed to solve the problem that the existing technology is inconvenient to effectively calculate the removable carbon sink of large seaweed. Summary of the invention

[0004] The object of the present invention is to provide a method for calculating the removable carbon sink of large seaweed, so as to realize the effective calculation of the removable carbon sink of large seaweed.

[0005] To achieve the above object, the present invention adopts the following technical solution: a method for calculating the removable carbon sink of large seaweed, which specifically comprises the following steps:

[0006] S1. Extracting the seaweed cultivation area in the cultivation area from the remote sensing image by classification extraction method;

[0007] S2. Use drones to collect samples from breeding areas and calculate the breeding area of ​​the sample points;

[0008] S3, by comparing the areas obtained in step S2 and step S1, the correction coefficient of the remote sensing image area of ​​the seaweed cultivation area is obtained, that is:

[0009]

[0010] Thus we can get: Corrected area of ​​seaweed cultivation area = statistical area interpreted by remote sensing × correction coefficient;

[0011] S4. Combined with the UAV image analysis and on-site survey results, the relationship between the corrected seaweed cultivation area and biomass was summarized to obtain the cultivation biomass;

[0012] S5. Calculate the removable carbon sink of seaweed according to the following formula:

[0013] Removable carbon sink = cultured biomass × dry weight × carbon content.

[0014] Furthermore, the classification extraction method in step S1 includes support vector machine, maximum likelihood classification method or deep learning method.

[0015] Furthermore, the relationship between kelp culture area and biomass is:

[0016]

[0017] Among them, W kelp is the production of kelp in the aquaculture area, in tons, S kelp This is the corrected size of the kelp farming area, in hectares.

[0018] Furthermore, the relationship between the cultivation area and biomass of Gracilaria is:

[0019]

[0020] Among them, W gracilaria is the production of Gracilaria in the breeding area, in tons, S gracilaria It is the corrected size of Gracilaria cultivation area, in hectares.

[0021] After adopting the above technical solution, it has the following beneficial effects compared with the existing technology: it can effectively reflect, calculate, correct and account for the removable carbon sink of large seaweed in the determined aquaculture area in the sea area, which has an important guiding role for the majority of aquaculture enterprises and collective units to realize the removable carbon sink accounting and has promotion value. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0023] Figure 1 is one of the training sample area distribution maps of the embodiment of the present invention;

[0024] Figure 2 This is the second training sample area distribution diagram of the embodiment of the present invention;

[0025] Figure 3 This is the third training sample area distribution diagram of the embodiment of the present invention;

[0026] Figure 4 This is the U-NET network framework diagram;

[0027] Figure 5 This is the distribution map of seaweed farming areas in Fujian Province;

[0028] Figure 6 This is one of the drone aerial photos of the area;

[0029] Figure 7 This is the second drone aerial photo of the area;

[0030] Figure 8 This is a schematic diagram of the relationship analysis between the breeding area and the number of seedling ropes in drone images;

[0031] Fig. 9 This is the remote sensing extraction result map of the key seaweed farming area (Xiapu) in Fujian Province. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0033] In view of the problems existing in the prior art, the present invention provides a method for calculating the removable carbon sink of large seaweed. The present invention is described in detail below in conjunction with the accompanying drawings.

[0034] Example

[0035] Fujian Province is located on the southeast coast of China. It has a coastline of nearly 4,000 kilometers, diverse terrain, mild and humid climate, which is suitable for the growth of seaweed. The vast sea area in this area and the good seawater quality provide excellent environmental conditions for the growth of large seaweed. At the same time, the coastal areas of Fujian have rich marine resources and diverse marine ecosystems, which provide rich nutrients and ecological support for seaweed cultivation. Kelp (Saccharina japonica) and Gracilaria (Gracilaria) are the two main seaweed species cultivated along the coast of Fujian. The cultivation of kelp and Gracilaria in Fujian Province ranks first in the country all year round, and is an important source of marine economy in Fujian Province. The large seaweed cultivation areas along the coast of Fujian are widely distributed, mainly concentrated in coastal cities such as Ningde, Fuzhou, Putian, Quanzhou and Zhangzhou and their surrounding waters. The breeding bases are usually built in areas with deep seas and moderate water currents. The cultivation of kelp and Gracilaria mainly relies on raft culture. Fujian Province has a rich coastline, and seaweed cultivation covers the coast of Fujian Province (except Xiamen City).

[0036] Remote sensing can understand the cultivation of large seaweed within a certain time frame, realize the monitoring of large seaweed cultivation, and truly reflect the scope and area of ​​cultivation, which plays an important role in the removable carbon sink accounting of large seaweed. At present, common classification methods or extraction methods can be used to identify the cultivation area. The supervised classification method is superior to the unsupervised classification method. In terms of methods, support vector machines, maximum likelihood classification methods or deep learning methods can be selected for classification and extraction. The extracted cultivation area more intuitively reflects the cultivation situation.

[0037] To investigate the cultivation of large seaweed (Gracilaria, Laminaria) along the coast of Fujian Province and to carry out the calculation of removable carbon sinks, remote sensing images must cover the coast of Fujian Province. According to the habit of seaweed cultivation in Fujian Province, Gracilaria is cultivated from late September to May of the following year, and Laminaria is cultivated in the sea from late December to May of the following year. Laminaria is easy to rot when the temperature rises, so it will be harvested before the weather gets hot. This example selects Planet satellite remote sensing images. The Planet remote sensing acquisition of the coast of Fujian Province was applied for through the education and scientific research plan of Planet Company.

[0038] In this example, a total of 9 remote sensing images were selected and applied for. The specific information of the remote sensing images is shown in Table 1. They cover 5 prefecture-level cities along the coast of Fujian Province, including some key bays and islands in Fujian Province, such as Sanduao and Sansha Bay in Ningde, Luoyuan Bay and Pingtan Island in Fuzhou, Xinghua Bay and Meizhou Island in Putian, Quanzhou Bay, Shenhu Bay and Weitou Bay in Quanzhou, Dongshan Bay and Zhao'an Bay in Zhangzhou, etc. The time distribution of remote sensing images is between early January and early April 2023, with the most concentrated distribution in January and March, during which time period there will be Gracilaria farming and kelp farming.

[0039]

[0040] Table 1 Remote sensing image information of seaweed cultivation areas along the coast of Fujian Province

[0041] Remote sensing images can be used to separate land and water through NDWI calculation. First, select the model training samples, taking Xiapu, Lianjiang and Zhangzhou as the analysis objects, and select Figure 1-3 The training samples shown ( Figure 1 It is the Sanduao area of ​​Xiapu County, Ningde City; Figure 2 Lianjiang District, Fuzhou City; Figure 3 The raft seaweed cultivation area was simultaneously delineated using ArcGIS. Figure 4 The deep learning U-NET architecture shown in the figure uses Python to crop remote sensing images, train models, and predict them. The training index evaluation results are shown in Table 2:

[0042]

[0043] Table 2 Results of model training evaluation indicators in Xiapu, Lianjiang and Zhangzhou regions of Fujian Province

[0044] The prediction results are converted into vector graphs and placed under the remote sensing image. Fig. 9 The three models are used to predict and extract the seaweed cultivation areas near the coast of Fujian Province. The distribution of seaweed cultivation is shown in Figure 5 As shown in the figure, the learning rate of the Xiapu and Zhangzhou models is better at 0.001, while the learning rate of the Lianjiang model is better at 0.0008. The loss rate and accuracy of the three models converged after 100 iterations, and no overfitting or underfitting was found.

[0045] The correction of the aquaculture area can be solved by using drones. Drone images are orthophotos, and the flying altitude is much lower than that of remote sensing satellites. The area counted by drones is closer to the actual aquaculture area. However, due to the limited power consumption and endurance of drones, drones cannot collect large-scale seaweed aquaculture areas, so they can only be used to collect sample points in the aquaculture area. The collected drone images can be visually interpreted, and the aquaculture area of ​​the sample points can be counted. Then, combined with the area counted by remote sensing images for comparison, the correction coefficient of the aquaculture area counted by remote sensing can be calculated, thereby realizing the correction of large-scale seaweed aquaculture areas.

[0046] In this example, Xiapu Wenqi Village and Lianjiang Guanwu Village were selected as the area correction areas using drone image collection. Figure 6-7 As shown ( Figure 6 It is Wenqi Village, Xiapu County, Fujian Province; Figure 7 The seaweed cultivation area in the drone-photographed areas of Xiapu and Lianjiang is 1.4489 km2 、1.4339km 2 , and the seaweed cultivation areas extracted from the Planet satellite images based on deep learning methods are 1.6684km 2 、1.6994km 2 The numerical ratio of the area manually interpreted by the drone to the area interpreted by remote sensing is the correction coefficient of the remote sensing image area of ​​the seaweed cultivation area, that is:

[0047]

[0048] Therefore, the area correction coefficients of the above two areas are 0.858 and 0.844 respectively. Since the drone shooting time was about one month after the seaweed harvesting in the two places, the seaweed area in the image taken by the drone at this time was smaller than the actual cultivation area, so the correction coefficient can be adjusted upward appropriately. The area correction coefficient of this study takes into account the average value of the actual area correction coefficient of the two places and the appropriate adjustment, and the final value is 0.87.

[0049] The corrected area of ​​seaweed cultivation area is the product of the statistical area interpreted by remote sensing and the correction coefficient, that is:

[0050] Corrected area of ​​seaweed cultivation area = statistical area interpreted by remote sensing × correction coefficient ②

[0051] In addition, the high resolution of drones can realize the conversion relationship between biomass and breeding area. The schematic diagram of the relationship analysis between the breeding area and the number of seedling ropes in drone images is as follows Figure 8 As shown. Combining the measurement and analysis of drone images and ecological surveys, it was found that the main seedling rope for kelp farming in Fujian Province is generally about 80m, and the results of remote sensing image interpretation show that the main seedling rope is also about this length. The length of the breeding seedling rope is about 10m, which is the distance between the two main seedling ropes. The distance between the breeding seedling ropes is about 4m, and about 20 breeding seedling ropes will be hung on one main seedling rope. Combining drone image analysis and on-site survey results, the relationship between kelp farming area and biomass can be summarized as:

[0052]

[0053] Among them, W kelp is the production (wet weight) of kelp in the aquaculture area, in tons (t), S kelp It is the corrected size of kelp culture area, in hectares (ha).

[0054] Gracilaria and kelp are in the same sea area. The main seedling rope for Gracilaria cultivation is 60-70m long, the length of the cultivation rope is about 7-8m, and the spacing between Gracilaria seedling ropes is 0.6m. Combining the analysis of drone images and the results of on-site investigation, the relationship between Gracilaria cultivation area and biomass can be summarized as follows:

[0055]

[0056] Among them, W gracilaria is the production (wet weight) of Gracilaria in the breeding area, in tons (t), S gracilaria It is the corrected size of Gracilaria culture area, in hectares (ha).

[0057] In this example, since remote sensing cannot classify kelp and Gracilaria, the aquaculture area is calculated separately based on the fishery statistical yearbook. The laboratory calculated that the dry weight rate of kelp aquaculture is about 10.29%, and the carbon content of dry weight kelp is about 24.47%. Gracilaria in Fujian Province is mainly cultivated as Gracilaria, with a dry weight rate of Gracilaria of about 15% and a carbon content of dry weight Gracilaria of about 31%. The removable carbon sink of seaweed is calculated by multiplying the biomass (wet weight), dry-wet ratio (dry weight rate), and carbon content, that is:

[0058] Removable carbon sink = cultured biomass × dry weight × carbon content ⑤

[0059] The above formula is used to calculate the aquaculture area, aquaculture output, removable carbon sink and carbon sink value of large seaweed (kelp, Gracilaria) along the coast of Fujian Province, as shown in Table 3 below:

[0060]

[0061]

[0062] Table 3 Data on seaweed (kelp, Gracilaria) culture area, removable carbon sink and carbon sink value along the coast of Fujian Province

[0063] The present invention can effectively reflect, calculate, correct and account for the removable carbon sink of large seaweed in a determined aquaculture area. If large seaweed can achieve carbon trading, this method will play an important guiding role in the realization of removable carbon sink accounting for aquaculture enterprises and collective units.

[0064] The above description is only used to illustrate the technical solution of the present invention rather than to limit it. Other modifications or equivalent substitutions made to the technical solution of the present invention by ordinary technicians in this field should be included in the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for calculating the removable carbon sink of large seaweed, characterized in that: The method specifically comprises the following steps: S1. Extracting the seaweed cultivation area in the cultivation area from the remote sensing image by classification extraction method; S2. Use drones to collect samples from breeding areas and calculate the breeding area of ​​the sample points; S3, by comparing the areas obtained in step S2 and step S1, the correction coefficient of the remote sensing image area of ​​the seaweed cultivation area is obtained, that is: Thus, we can obtain: Corrected area of ​​seaweed cultivation area = statistical area interpreted by remote sensing × correction coefficient; S4. Combined with the UAV image analysis and on-site survey results, the relationship between the corrected seaweed cultivation area and biomass was summarized to obtain the cultivation biomass; S5. Calculate the removable carbon sink of seaweed according to the following formula: Removable carbon sink = cultured biomass × dry weight × carbon content.

2. The method for calculating the removable carbon sink of large seaweed according to claim 1, characterized in that: The classification extraction method described in step S1 includes support vector machine, maximum likelihood classification method or deep learning method.

3. The method for calculating the removable carbon sink of large seaweed according to claim 1, characterized in that: The relationship between kelp culture area and biomass is: Among them, W kelp is the production of kelp in the aquaculture area, in tons, S kelp This is the corrected size of the kelp farming area, in hectares.

4. The method for calculating the removable carbon sink of large seaweed according to claim 1, characterized in that: The relationship between the cultivation area and biomass of Gracilaria is: Among them, W gracilaria is the production of Gracilaria in the breeding area, in tons, S gracilaria It is the corrected size of Gracilaria cultivation area, in hectares.

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