Method and system for estimating carbon sequestration and oxygen release capabilities of reed vegetation

Abstract

The invention discloses a reed vegetation carbon fixation and oxygen release capability estimation method and system, and relates to the field of remote sensing technology and vegetation biomass extraction, and the method comprises the steps: obtaining the actually measured biomass and remote sensing data of to-be-estimated reed vegetation in a target region in different seasons; extracting a vegetation index, analyzing the vegetation index and the biomass to obtain a biomass model, and determining an optimal biomass estimation model in different seasons; obtaining overground biomass according to the optimal biomass estimation model in different seasons, estimating underground biomass by adopting a different-speed growth model, and determining total biomass of reed vegetation in different seasons; according to the total biomass of the reed vegetation in different seasons, the carbon sequestration amount of the reed vegetation is obtained by adopting a biomass carbon sequestration amount conversion model, and the carbon sequestration and oxygen release amount of the reed vegetation is determined in combination with a photosynthesis reaction equation. Based on the remote sensing image, the total biomass of the reed is obtained through the biomass model and the different-speed growth model, so that the carbon sequestration and oxygen release capacity of the reed is estimated.

Description

Technical Field

[0001] The invention relates to the fields of remote sensing technology and wetland vegetation biomass extraction, and in particular to a method and system for estimating the carbon fixation and oxygen release capacity of reed vegetation. Background Art

[0002] Due to human demand for energy, greenhouse gases such as CO2 are emitted into the atmosphere, exacerbating ecological and economic problems such as global warming and glacier melting. Early studies on natural vegetation carbon sinks focused on forest, grassland, agricultural and other ecosystems, with less research on the carbon storage and carbon sequestration capacity of wetland vegetation ecosystems.

[0003] Wetlands are extremely precious natural treasures on Earth and are critical to human survival and prosperity. They are one of the most important ecological environments for humans. In the intricate interweaving of wetland ecosystems, wetland plants play a central role in capturing and fixing carbon dioxide. The biomass of these plants is not only a key criterion for judging the primary production capacity of their communities, but also has a crucial impact on evaluating the carbon storage capacity and carbon fixation efficiency of wetlands.

[0004] Traditionally, biomass is obtained through field surveys, harvest statistics, and empirical predictions. Although these methods have high estimation accuracy, they are time-consuming, labor-intensive, and highly destructive, making it difficult to achieve large-scale biomass monitoring of wetland plants. Currently, remote sensing technology is mostly used to obtain biomass. Remote sensing technology can quickly and widely extract wetland information, and it is widely used to estimate vegetation biomass. To a large extent, it makes up for the shortcomings of traditional biomass estimation methods.

[0005] Zhang Yuxin, Huang Jianxi, Jin Yunxiang and others used vegetation index and leaf area index (LAI) as ground biophysical parameters of grassland aboveground biomass, and constructed coupled remote sensing observation and mechanism models through data assimilation. Han Yongting and Han Ying used Landsat5TM remote sensing technology to develop a prediction model for the biomass of meadow wetlands and swamp wetlands in Honghe Wetland, further integrated land cover data into these models, and applied them in practice, thereby predicting and analyzing the surface biomass of meadow wetlands and swamp wetlands.

[0006] However, since reeds are mainly distributed in nature reserves, field surveys are limited and their area is huge, resulting in poor accuracy in estimating the carbon fixation and oxygen release capacity of reeds from field surveys. Summary of the invention

[0007] The present invention provides a method and system for estimating the carbon fixation and oxygen release capacity of reed vegetation, so as to solve the above-mentioned problem existing in the prior art, that is, the problem that the accuracy of estimating the carbon fixation and oxygen release capacity in the prior art is poor.

[0008] The present invention adopts the following technical solutions:

[0009] The present invention provides a method for estimating the carbon fixation and oxygen release capacity of reed vegetation, comprising:

[0010] Obtain the measured biomass and remote sensing images of the reed vegetation to be estimated in the target area in different seasons;

[0011] Extract vegetation index from remote sensing images, perform multiple univariate linear and curvilinear regression analyses on vegetation index and biomass, and obtain biomass model;

[0012] The effectiveness of biomass models with different vegetation indices was evaluated, and the biomass model with the highest determination coefficient among different vegetation indices was selected for accuracy evaluation to obtain the optimal biomass estimation model with the highest estimation accuracy.

[0013] The aboveground biomass in different seasons was obtained by using the optimal biomass estimation model with the highest estimation accuracy in different seasons, and the underground biomass was estimated by using the allometric growth model to determine the total biomass of reed vegetation in different seasons.

[0014] According to the total biomass of reed vegetation in different seasons, the biomass carbon fixation conversion model was used to obtain the carbon fixation of reed vegetation in different seasons. Combined with the photosynthesis reaction equation, the carbon fixation and oxygen release of reed vegetation were determined.

[0015] Also includes:

[0016] Before extracting vegetation index from remote sensing images, the remote sensing data are subjected to radiation calibration, atmospheric correction, image mosaicking and cropping to obtain cropped remote sensing images, and multi-scale segmentation methods are used to perform multi-scale segmentation on the cropped remote sensing images.

[0017] Optionally, the biomass model is obtained by performing multiple single variable linear and curvilinear regression analyses on the vegetation index and biomass, specifically including:

[0018] Using vegetation index as the independent variable and biomass as the dependent variable, various univariate linear and curvilinear regression analyses were performed to obtain the biomass model.

[0019] Optionally, the step of selecting a biomass model with the highest determination coefficient among different vegetation indices for accuracy assessment to obtain an optimal biomass estimation model with the highest estimation accuracy specifically includes:

[0020] The mean relative error (MRE) and root mean square error (RMSE) were used to estimate the accuracy of the biomass model with the highest determination coefficient among different vegetation indices. The biomass model with the highest accuracy was selected to fit the measured biomass to obtain the optimal biomass estimation model with the highest estimation accuracy in different seasons.

[0021] Optionally, the estimating underground biomass by using an allometric growth model specifically includes:

[0022] The underground biomass of reed was obtained using the following formula:

[0023] Y=aX b

[0024] Among them, a and b are model parameters, Y is the underground biomass of reed, and X is the aboveground biomass of reed.

[0025] Optionally, the biomass carbon fixation conversion model is used to obtain the carbon fixation of reed vegetation in different seasons, and the carbon fixation and oxygen release are determined in combination with the photosynthesis reaction formula, specifically including:

[0026] The carbon sequestration of reed vegetation is obtained using the following formula:

[0027] C i = pA i Q i

[0028] Among them, C i is the carbon sequestration of reed vegetation, A i is the distribution area of ​​reed vegetation, Q i is the total biomass of reed vegetation, p is the carbon conversion coefficient;

[0029] According to the photosynthesis reaction 6CO2+6H2O→C6H2O6+6O2, the carbon fixation amount is multiplied by the correlation coefficient to obtain the vegetation CO2 absorption and O2 release, and the carbon fixation and oxygen release amount are determined.

[0030] The present invention provides a system for estimating the carbon fixation and oxygen release capacity of reed vegetation, comprising:

[0031] The first module is used to obtain the measured biomass and remote sensing images of the reed vegetation to be estimated in the target area in different seasons;

[0032] The second module is used to extract vegetation index from remote sensing images, perform multiple single variable linear and curvilinear regression analyses on vegetation index and biomass, and obtain a biomass model;

[0033] The third module is used to evaluate the effectiveness of biomass models of different vegetation indices, select the biomass model with the highest determination coefficient among different vegetation indices for accuracy evaluation, and obtain the optimal biomass estimation model with the highest estimation accuracy;

[0034] The fourth module is used to obtain the aboveground biomass in different seasons according to the optimal biomass estimation model with the highest estimation accuracy in different seasons, estimate the underground biomass using the allometric growth model, and determine the total biomass of reed vegetation in different seasons;

[0035] The fifth module is used to obtain the carbon fixation amount of reed vegetation in different seasons based on the total biomass of reed vegetation in different seasons by using the biomass carbon fixation conversion model, and determine the carbon fixation and oxygen release of reed vegetation in combination with the photosynthesis reaction equation.

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides a method for estimating the carbon fixation and oxygen release capacity of reed vegetation, which obtains the measured biomass and remote sensing data of the reed vegetation to be estimated in the target area in different seasons, extracts the vegetation index, selects the model with the highest vegetation index determination coefficient for accuracy evaluation, and obtains the biomass estimation model. The aboveground average biomass of the reed vegetation is inverted through the biomass estimation model, so that a relatively accurate aboveground biomass of the reed vegetation can be obtained; then, based on the aboveground biomass, the underground biomass is estimated using an allometric growth model, and then the aboveground biomass and the underground biomass can be combined to obtain a relatively accurate total biomass of the reed vegetation; finally, based on the total biomass of the reed vegetation, the carbon fixation amount of the reed vegetation is obtained through a biomass carbon fixation conversion model, and combined with the photosynthesis reaction equation, the carbon fixation and oxygen release of the reed vegetation can be determined, thereby improving the accuracy of estimating the carbon fixation and oxygen release capacity of the reed. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0038] Figure 1 A flow chart of a method for estimating the carbon fixation and oxygen release capacity of reed vegetation provided in an embodiment of the present invention;

[0039] Figure 2 A segmentation diagram of a plant community at different segmentation scales provided by an embodiment of the present invention;

[0040] Figure 3 A graph showing changes in carbon fixation in the Liaohe River estuary in different seasons provided in an embodiment of the present invention.

[0041] The above drawings have shown clear embodiments of the present invention, which will be described in more detail below. These drawings and text descriptions are not intended to limit the scope of the present invention in any way, but to illustrate the concept of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] Reed (Phragmites Communisreed), a large grass plant, has formed a dominant community in the wetland and has become the primary producer of the Liaohe Estuary coastal wetland. Not only does it have the largest area, but its carbon storage and carbon fixation capacity cannot be underestimated. Its strong primary productivity and biomass play a vital role in the ecosystem. It is of great significance to carry out research on carbon fixation in the Liaohe Estuary wetland.

[0044] At present, the amount of carbon sequestration is obtained by multiplying the vegetation biomass and the vegetation distribution area with the carbon conversion coefficient, which is calculated by combining actual measurements with previous experience. Therefore, accurately estimating the amount of carbon sequestration in wetland vegetation is an essential key issue.

[0045] In view of the above problems, the present invention provides a method for estimating the carbon fixation and oxygen release capacity of reed vegetation. The method classifies the reed vegetation in the Liaohe Estuary Wetland through remote sensing satellites and ENVI software, and studies the biomass of the reed community in the Liaohe Estuary coastal wetland in combination with a biomass inversion model and an allometric growth relationship model. Finally, the total above-ground and underground biomass and the carbon conversion coefficient of the reed vegetation are used to obtain the carbon fixation amount and carbon fixation and oxygen release capacity of the coastal wetland.

[0046] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0047] Figure 1 The present invention provides a flowchart of a method for estimating the carbon fixation and oxygen release capacity of reed vegetation. The present invention classifies reed vegetation in Liaohe Estuary Wetland through remote sensing satellites and ENVI software, and studies the biomass of reed communities in Liaohe Estuary coastal wetland in combination with a biomass inversion model and an allometric growth relationship model. Finally, the carbon fixation and carbon fixation and oxygen release capacity of coastal wetlands are obtained through the total biomass of reed vegetation and the carbon conversion coefficient. Figure 1 As shown, this embodiment shows a method for estimating the carbon fixation and oxygen release capacity of reed vegetation, including:

[0048] S101: Obtain the measured biomass and remote sensing images of the reed vegetation to be estimated in the target area in different seasons.

[0049] For example, Landsat 8OLI satellite can be used to obtain metadata of vegetation in the coastal wetland at the mouth of the Liaohe River. A portable GPS can be selected to locate the sampling area, and the longitude and latitude can be read. The target area size can be selected as 1m×1m, and reed vegetation can be collected.

[0050] The collection time of reed vegetation is basically consistent with the Landsat8OLI image time. Since the distribution of reeds is different in different years, sampling points in the summer, autumn and winter of 2017 were selected in the continuous reed area of ​​the sample plot in the study area to show the distribution characteristics of the research object.

[0051] S102: Extract vegetation index from remote sensing images, perform multiple single variable linear and curve regression analyses on vegetation index and biomass, and obtain a biomass model.

[0052] For example, based on the sampling points in summer, autumn and winter, multiple image bands of the Liaohe River estuary can be superimposed in combination with the reed vegetation corresponding to the actual coordinates, the vegetation index can be obtained using the ENVI software, and then the reed vegetation can be selected using an object-oriented multi-scale segmentation method; finally, a biomass model can be obtained by performing multiple univariate linear and curvilinear regression analyses on the vegetation index and biomass.

[0053] Optionally, before extracting vegetation indices from remote sensing images, the acquired remote sensing data are subjected to radiation calibration, atmospheric correction, image mosaicking and cropping.

[0054] Exemplarily, an object-oriented multi-scale segmentation method is used to select reed vegetation; wherein the segmentation scale for selecting reed vegetation can be, for example, a 100 segmentation scale, a 50 segmentation scale, and a 10 segmentation scale, such as Figure 2 (a) to Figure 2 as shown in (c).

[0055] Optionally, the effectiveness of biomass models with different vegetation indices is evaluated, the model with the highest coefficient of determination is selected, the mean relative error (MRE) and root mean square error (RMSE) are used for accuracy estimation, and the model with the highest accuracy is selected to fit the measured biomass to obtain the optimal biomass estimation model.

[0056] The selected vegetation indices include the normalized difference vegetation index NDVI, the difference vegetation index RVI, the ratio vegetation index RVI, the enhanced vegetation index EVI, the soil adjusted vegetation index SAVI and the modified soil adjusted vegetation index MSAVI.

[0057] The above vegetation index band formula is shown in Table 1.

[0058] Table 1 Calculation formula of vegetation index band

[0059]

[0060] Optionally, the biomass model includes a linear model, a logarithmic model, a reciprocal model, a quadratic polynomial model, a cubic polynomial equation model, a power model, a S-type model and an exponential model.

[0061] Based on remote sensing data, a regression model was established for vegetation index with vegetation index as the independent variable and biomass as the dependent variable, see Table 2.

[0062] Table 2 Regression analysis equation

[0063]

[0064] S103: Evaluate the effectiveness of biomass models of different vegetation indices, select the biomass model with the highest determination coefficient among different vegetation indices for accuracy evaluation, and obtain the optimal biomass estimation model with the highest estimation accuracy.

[0065] By selecting the model with the highest determination coefficient, the mean relative error (MRE) and root mean square error (RMSE) were used to estimate the accuracy. The model with the highest accuracy was selected to fit the measured biomass to obtain the biomass estimation model.

[0066] Exemplarily, verification points can be randomly selected to perform accuracy test estimation on the optimal model among the above six vegetation indices, and the model with the largest determination coefficient in the estimation model of each vegetation index can be accuracy verified, and its mean relative error MRE and root mean square error RMSE can be calculated for accuracy estimation.

[0067]

[0068] Using Landsat8OLI data and the corresponding biomass data, the model with the best correlation coefficient and the highest determination coefficient was selected for accuracy test, and the regression accuracy test results for the summer of 2017-2021 were obtained.

[0069] Based on the accuracy comparison of the models, it can be found that the RVI accuracy is relatively higher and the EVI accuracy is relatively lower. Referring to the year with the highest accuracy among the six summer indices and fitting with the measured biomass, the biomass model inversion formula from 2017 to 2021 is obtained as follows:

[0070] y=3.375x 3 -48.89x 2 +2239.7x-3495.6(2017)

[0071] y=5.622x 3 -78.251x2 +2276.2x+2890(2018)

[0072] y=4.388x 3 -47.076x 2 +1749.6x-698.85(2019)

[0073] y=6.5108x 3 -110.77x 2 +498.6x+521.27(2020)

[0074] y=6.865x 3 +24.219x 2 -920.69x+1548(2021)

[0075] According to the biomass model inversion formula, the summer aboveground biomass inversion distribution can be obtained, and then the summer aboveground biomass from 2017 to 2021 can be obtained, as shown in Table 3-1:

[0076] Table 3-1 Aboveground biomass in summer from 2017 to 2021

[0077]

[0078] Based on this, the same method was used to obtain the inverted distribution of aboveground biomass in autumn and winter from 2017 to 2021, and then the aboveground biomass in autumn and winter was obtained. I will not go into details here. The aboveground biomass in autumn and winter are shown in Table 3-2 and Table 3-3.

[0079] Table 3-2 Aboveground biomass in autumn from 2017 to 2021

[0080]

[0081] Table 3-3 Aboveground biomass in winter from 2017 to 2021

[0082]

[0083] Based on the biomass of summer, autumn and winter, the average total aboveground biomass in the study area was obtained by taking the average value.

[0084] By preprocessing the remote sensing data, the remote sensing images of the study area can be accurately extracted, ensuring the accuracy and availability of the remote sensing image data; the extraction of vegetation index can better monitor and analyze the growth and distribution of reed vegetation; the object-oriented multi-scale segmentation method is used to select reed vegetation, which can make the distinction more accurate.

[0085] S104: Obtain the aboveground biomass in different seasons according to the optimal biomass estimation model with the highest estimation accuracy in different seasons, estimate the underground biomass using an allometric growth model, and determine the total biomass of reed vegetation in different seasons.

[0086] Specifically, by investigating the reed biomass in summer, autumn and winter in different years and periods in the field, six vegetation indices with the best correlation fit were selected, and eight biomass models, including linear, quadratic, cubic, logarithmic, exponential, inverse, power and S-type, were constructed using the optimal vegetation indices. The aboveground biomass of the reed was estimated by regressing the optimal model using the biomass model. By analyzing the aboveground and underground biomass patterns of the reed wetland, a model of the allometric growth relationship between the aboveground and underground biomass of the reed wetland was established, and the underground biomass distribution map was analyzed by comparing the comprehensive data of the aboveground biomass. Finally, the aboveground and underground biomass were superimposed to construct the total biomass of the Liaohekou reed wetland.

[0087] Optionally, the estimating underground biomass by using an allometric growth model specifically includes:

[0088] The underground biomass of reed was obtained using the following formula:

[0089] Y=aX b

[0090] Among them, a and b are model parameters, Y is the underground biomass of reed, and X is the aboveground biomass of reed.

[0091] The following is a detailed explanation of the above-ground and underground biomass distribution pattern of coastal wetland reed communities in the summer of 2017-2021, the construction of their allometric growth relationship model, and the estimation of the underground biomass of reed communities; accordingly, the above-ground and underground biomass distribution pattern in autumn and winter, the construction of their allometric growth relationship model, and the estimation of the underground biomass of reed communities are obtained in the same way.

[0092] The above-ground and underground biomass of reeds in summer were distributed. Based on the measured biomass data, the distribution of reed biomass in summer from 2017 to 2021 was obtained, as shown in Table 4-1:

[0093] Table 4-1 Statistics of reed biomass in summer

[0094]

[0095] According to the total sample data of five years, the average ratio of available aboveground biomass to total biomass is about 70%, and the average ratio of underground biomass to total biomass is about 30%.

[0096] After allocating the aboveground and underground biomass of reed in summer, according to the allometric growth equation and combined with the measured biomass in summer from 2017 to 2021, the allometric growth model of aboveground and underground biomass of reed in summer was obtained, as shown below:

[0097] y=8.1712x 0.7631 (2017)

[0098] y=6.5408x 0.8074 (2018)

[0099] y=2.6457x 0.9248 (2019)

[0100] y=7.8462x 0.7682 (2020)

[0101] y=3.3598 0.8764 (2021)

[0102] Based on the Landsat8OLI image data, the average above-ground biomass of reeds in the summer of 2017-2021 was inverted, and combined with the allometric growth model corresponding to the summer of each year, the average underground biomass of reeds in the summer of 2017-2021 was obtained, see Table 4-2.

[0103] Table 4-2 Average biomass in summer

[0104]

[0105] The same method was used to obtain the distribution of reed biomass in autumn from 2017 to 2021, as shown in Table 4-3.

[0106] Table 4-3 Statistics of reed biomass in autumn

[0107]

[0108] Similarly, according to the allometric growth equation, combined with the measured biomass in autumn from 2017 to 2021, the allometric growth model of aboveground and underground biomass of reed in autumn was obtained, as shown below:

[0109] y=2.6991x 0.8415 (2017)

[0110] y=1.3918x 0.9156 (2018)

[0111] y=1.6389x 0.8942 (2019)

[0112] y=7.886x 0.7022 (2020)

[0113] y=2.2887x 0.845 (2021)

[0114] Similarly, the average underground biomass of reeds in autumn 2017-2021 was obtained, see Table 4-4.

[0115] Table 4-4 Average biomass in autumn

[0116]

[0117] The same method was used to obtain the winter reed biomass distribution from 2017 to 2021, as shown in Table 4-5.

[0118] Table 4-5 Statistics of reed biomass in winter

[0119]

[0120] Similarly, the allometric growth model of aboveground and underground biomass of reed in winter is obtained as follows:

[0121] y=0.1524x 1.1942 (2017)

[0122] y=4.6411x 0.7 (2018)

[0123] y=0.6116x 1.0264 (2019)

[0124] y=0.571x 0.9824 (2020)

[0125] y=0.6239x 0.9683 (2021)

[0126] Similarly, the average underground biomass of reeds in winter 2017-2021 was obtained, see Table 4-6.

[0127] Table 4-6 Average biomass in winter

[0128]

[0129] The average underground biomass is obtained by averaging the underground biomass, and then the total biomass can be obtained. This step constructs an allometric relationship model by analyzing the aboveground and underground biomass distribution pattern of the reed community in the Liaohe Estuary Coastal Wetland, and estimates the underground biomass of the reed community in the study area based on the aboveground biomass. The average underground biomass is obtained by averaging the underground biomass. The total biomass can be obtained through the underground average biomass, which provides a basis for the carbon fixation of reed vegetation.

[0130] S105: Based on the total biomass of reed vegetation in different seasons, the biomass carbon fixation conversion model is used to obtain the carbon fixation of reed vegetation in different seasons, and combined with the photosynthesis reaction formula, the carbon fixation and oxygen release of reed vegetation are determined.

[0131] In this step, the carbon fixation of reed vegetation in the Liaohe River Estuary is obtained based on the conversion relationship between biomass and carbon fixation. The overall changes in carbon fixation in summer, autumn and winter over a five-year period are analyzed. Based on the annual average carbon fixation, the annual oxygen release of reed vegetation in the Liaohe River Estuary wetland is obtained.

[0132] Optionally, the biomass carbon fixation conversion model is used to obtain the carbon fixation and oxygen release capacity of reed vegetation, specifically including:

[0133] The carbon fixation and oxygen release capacity includes carbon fixation amount and carbon fixation and oxygen release amount;

[0134] The carbon sequestration of reed vegetation is obtained using the following formula:

[0135] C i = pA i Q i

[0136] Among them, C i is the carbon sequestration of reed vegetation, A i is the distribution area of ​​reed vegetation, Q i is the total biomass of reed vegetation, and p is the carbon conversion coefficient.

[0137] According to the photosynthesis reaction equation 6CO2+6H2O→C6H2O6+6O2, the carbon fixation amount is multiplied by the correlation coefficient to obtain the carbon fixation and oxygen release amount.

[0138] Specifically, based on the biomass and biomass to carbon fixation conversion model, the changes in carbon fixation in the study area in summer were obtained, as shown in Table 5-1.

[0139] Table 5-1 Changes in carbon fixation in Liaohe Estuary in summer (t)

[0140]

[0141] It can be seen from Table 5-1 that the carbon fixation of the aboveground part of reed in summer is greater than that of the underground part, so the aboveground part of reed in summer is the main part of carbon fixation; in the five years from 2017 to 2021, the total carbon fixation of reed in summer showed a trend of first decreasing, then increasing, and then stabilizing. The total carbon fixation reached a peak in 2017 and the minimum value appeared in 2020. The changes in carbon fixation in the Liaohe Estuary in summer can be seen. Figure 3 (a).

[0142] Based on the biomass and biomass to carbon fixation conversion model, the changes in carbon fixation in the study area in autumn were obtained, as shown in Table 5-2.

[0143] Table 5-2 Changes in carbon fixation in Liaohe Estuary in autumn (t)

[0144]

[0145] As shown in Table 5-2, the carbon fixation of the aboveground part of reed in autumn is less than that of the underground part, so the underground part of reed in autumn is the main part of carbon fixation. In the past five years, the total carbon fixation of reed in autumn showed a trend of first decreasing, with the total carbon fixation reaching a peak in 2017 and the minimum value appearing in 2021. The changes in carbon fixation in the Liaohe Estuary in autumn are shown in Table 5-2. Figure 3 (b).

[0146] Based on the biomass and biomass to carbon fixation conversion model, the changes in carbon fixation in the study area in winter were obtained, as shown in Table 5-3.

[0147] Table 5-3 Changes in carbon fixation in Liaohe Estuary in winter (t)

[0148]

[0149] As shown in Table 5-3, the carbon fixation of reeds above ground in winter is less than that of the underground part, so the underground part of reeds in winter is the main part of carbon fixation. In the past five years, the total carbon fixation of reeds in winter showed a trend of first decreasing, then increasing, and then decreasing. The total carbon fixation reached a peak in 2019 and the minimum value was in 2021. The changes in carbon fixation in Liaohe River estuary in winter can be seen. Figure 3 (c).

[0150] Finally, according to the carbon fixation amount and the photosynthesis reaction equation 6CO2+6H2O→C6H2O6+6O2, the vegetation carbon storage is multiplied by 3.67 and 2.67 respectively, and the total amount of CO2 absorbed by the vegetation and the amount of O2 released can be obtained, and then the carbon fixation and oxygen release amount can be obtained, thereby estimating the carbon fixation and oxygen release capacity of reed vegetation.

[0151] The above is a method for estimating the carbon fixation and oxygen release capacity of reed vegetation provided by one or more embodiments of this specification. Based on the same idea, this specification also provides a corresponding system for estimating the carbon fixation and oxygen release capacity of reed vegetation, including:

[0152] The first module is used to obtain the measured biomass and remote sensing images of the reed vegetation to be estimated in the target area in different seasons;

[0153] The second module is used to extract vegetation index from remote sensing images, perform multiple single variable linear and curvilinear regression analyses on vegetation index and biomass, and obtain a biomass model;

[0154] The third module is used to evaluate the effectiveness of biomass models of different vegetation indices, select the biomass model with the highest determination coefficient among different vegetation indices for accuracy evaluation, and obtain the optimal biomass estimation model with the highest estimation accuracy;

[0155] The fourth module is used to obtain the aboveground biomass in different seasons according to the optimal biomass estimation model with the highest estimation accuracy in different seasons, estimate the underground biomass using the allometric growth model, and determine the total biomass of reed vegetation in different seasons;

[0156] The fifth module is used to obtain the carbon fixation amount of reed vegetation in different seasons based on the total biomass of reed vegetation in different seasons by using the biomass carbon fixation conversion model, and determine the carbon fixation and oxygen release of reed vegetation in combination with the photosynthesis reaction equation.

[0157] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0158] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present invention.

Claims

1. A method for estimating the carbon fixation and oxygen release capacity of reed vegetation, characterized in that: include: Obtain the measured biomass and remote sensing images of the reed vegetation to be estimated in the target area in different seasons; Extract vegetation index from remote sensing images, perform multiple single variable linear and curvilinear regression analyses on vegetation index and biomass, and obtain biomass model; The effectiveness of biomass models with different vegetation indices was evaluated, and the biomass model with the highest determination coefficient among different vegetation indices was selected for accuracy evaluation to obtain the optimal biomass estimation model with the highest estimation accuracy. The aboveground biomass in different seasons was obtained by using the optimal biomass estimation model with the highest estimation accuracy in different seasons, and the underground biomass was estimated by using the allometric growth model to determine the total biomass of reed vegetation in different seasons. According to the total biomass of reed vegetation in different seasons, the biomass carbon fixation conversion model was used to obtain the carbon fixation of reed vegetation in different seasons. Combined with the photosynthesis reaction equation, the carbon fixation and oxygen release of reed vegetation were determined.

2. The method for estimating the carbon fixation and oxygen release capacity of reed vegetation as claimed in claim 1, characterized in that: Also includes: Before extracting vegetation index from remote sensing images, the remote sensing data are subjected to radiation calibration, atmospheric correction, image mosaicking and cropping to obtain cropped remote sensing images, and multi-scale segmentation methods are used to perform multi-scale segmentation on the cropped remote sensing images.

3. The method for estimating the carbon fixation and oxygen release capacity of reed vegetation as claimed in claim 1, characterized in that: The biomass model is obtained by performing multiple single variable linear and curvilinear regression analyses on the vegetation index and biomass, specifically including: Using vegetation index as the independent variable and biomass as the dependent variable, various univariate linear and curvilinear regression analyses were performed to obtain the biomass model.

4. The method for estimating the carbon fixation and oxygen release capacity of reed vegetation as claimed in claim 1, characterized in that: The method of selecting the biomass model with the highest determination coefficient among different vegetation indices for accuracy assessment to obtain the optimal biomass estimation model with the highest estimation accuracy specifically includes: The mean relative error (MRE) and root mean square error (RMSE) were used to estimate the accuracy of the biomass model with the highest determination coefficient among different vegetation indices. The biomass model with the highest accuracy was selected to fit the measured biomass to obtain the optimal biomass estimation model with the highest estimation accuracy in different seasons.

5. The method for estimating the carbon fixation and oxygen release capacity of reed vegetation as claimed in claim 1, characterized in that: The estimation of underground biomass by using the allometric growth model specifically includes: The underground biomass of reed was obtained using the following formula: Y=aX b Among them, a and b are model parameters, Y is the underground biomass of reed, and X is the aboveground biomass of reed.

6. The method for estimating the carbon fixation and oxygen release capacity of reed vegetation as claimed in claim 1, characterized in that: The biomass carbon fixation conversion model is used to obtain the carbon fixation of reed vegetation in different seasons, and the carbon fixation and oxygen release are determined in combination with the photosynthesis reaction formula, specifically including: The carbon sequestration of reed vegetation is obtained using the following formula: C i =pA i Q i Among them, C i is the carbon sequestration of reed vegetation, A i is the distribution area of ​​reed vegetation, Q i is the total biomass of reed vegetation, p is the carbon conversion coefficient; According to the photosynthesis reaction 6CO2+6H2O→C6H2O6+6O2, the carbon fixation amount is multiplied by the correlation coefficient to obtain the vegetation CO2 absorption and O2 release, and the carbon fixation and oxygen release amount are determined.

7. A system for estimating the carbon fixation and oxygen release capacity of reed vegetation, characterized in that: include: The first module is used to obtain the measured biomass and remote sensing images of the reed vegetation to be estimated in the target area in different seasons; The second module is used to extract vegetation index from remote sensing images, perform multiple single variable linear and curvilinear regression analyses on vegetation index and biomass, and obtain a biomass model; The third module is used to evaluate the effectiveness of biomass models of different vegetation indices, select the biomass model with the highest determination coefficient among different vegetation indices for accuracy evaluation, and obtain the optimal biomass estimation model with the highest estimation accuracy; The fourth module is used to obtain the aboveground biomass in different seasons according to the optimal biomass estimation model with the highest estimation accuracy in different seasons, estimate the underground biomass using the allometric growth model, and determine the total biomass of reed vegetation in different seasons; The fifth module is used to obtain the carbon fixation amount of reed vegetation in different seasons based on the total biomass of reed vegetation in different seasons by using the biomass carbon fixation conversion model, and determine the carbon fixation and oxygen release of reed vegetation in combination with the photosynthesis reaction equation.

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