Method for monitoring and evaluating carbon sequestration capability of island ecosystem

Abstract

The invention relates to the technical field of carbon sequestration capacity monitoring, in particular to a carbon sequestration capacity monitoring and evaluating method for an island ecosystem, which comprises the following steps: S1, multi-source data fusion and habitat partitioning: dividing an island habitat type partitioning map; s2, stratified sampling and carbon reserve actual measurement: measuring the carbon content, and constructing a carbon reserve actual measurement database; s3, constructing a dynamic carbon sink model: carrying out a whole growth cycle environment simulation experiment on sea grass and algae in different habitats, and establishing the dynamic carbon sink model taking habitat types as classification units; s4, carbon sequestration capability quantification and bottleneck diagnosis: calculating carbon sequestration contribution values of various habitat types, identifying low-efficiency carbon sequestration areas through contribution value comparison, and analyzing environmental limitation factors of the low-efficiency carbon sequestration areas; and S5, integrating a sink increasing technology and generating a path: designing a habitat specific sink increasing scheme for the identified low-efficiency carbon sink area. According to the invention, high-precision monitoring, intelligent evaluation and system optimization of the island ecosystem carbon sequestration capability are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon sink capacity monitoring, and particularly to a method for monitoring and evaluating the carbon sink capacity of island ecosystems. Background Art

[0002] With the continuous intensification of global climate change pressure, enhancing the carbon sink capacity of natural ecosystems has become an important path to mitigate the atmospheric carbon dioxide concentration. As an important part of blue carbon sinks, island ecosystems are characterized by rich vegetation, widespread seagrasses and algae, and stable sedimentary environments, playing a significant role in carbon fixation, carbon burial, and atmospheric CO2 emission reduction. In recent years, with the development of remote sensing technology, ecological modeling, and intelligent analysis means, carrying out precise monitoring and dynamic evaluation of the carbon sink potential of island ecosystems has become a key technical direction for ecological protection and carbon sink governance.

[0003] Existing island carbon sink monitoring methods generally have problems such as low data resolution, rough habitat division, and weak dynamic capabilities. Some methods rely only on single-source remote sensing images or static survey data, making it difficult to comprehensively depict the carbon sink process in the complex multi-habitat structure of islands; carbon storage estimation is mostly based on empirical coefficients, lacking parameter support based on actual measurements; in addition, existing systems generally lack carbon sink difference modeling and response mechanism analysis for different habitat types, unable to achieve precise identification of low-efficiency carbon sink areas and optimization of intervention paths, severely restricting the implementation and application of enhancing ecological carbon sequestration capacity. Summary of the Invention

[0004] The present invention provides a method for monitoring and evaluating the carbon sink capacity of island ecosystems, realizing comprehensive monitoring, precise diagnosis, and optimized improvement of the carbon sink capacity of island ecosystems, and serving ecosystem management and carbon sequestration decision-making.

[0005] A method for monitoring and evaluating the carbon sink capacity of island ecosystems includes the following steps: S1, multi-source data fusion and habitat zoning: Obtain remote sensing images and three-dimensional terrain data of the island through satellite remote sensing, unmanned aerial vehicle scanning, and lidar. Perform cloud removal processing, band fusion, and spatial registration on the remote sensing images, and divide the habitat type zoning map of the island. The habitat types include island land vegetation areas, intertidal salt marshes, shallow sea seagrass beds, and algal areas; S2, stratified sampling and actual measurement of carbon storage: Based on the habitat type zoning map of the island, lay out sampling points by layer according to habitat types, collect aboveground / underground biomass of vegetation, sediment column samples, and seagrass / algae living samples, measure the carbon content, and construct an actual measurement database of carbon storage including biomass carbon density, sediment carbon flux, and growth cycle parameters; S3. Dynamic carbon sink model construction: Based on the measured carbon storage database, conduct environmental simulation experiments on seagrasses and algae in different habitats throughout their growth cycles, extract the correlation rules between light, water temperature, salinity, and carbon absorption rate, and establish a dynamic carbon sink model with habitat types as classification units; S4. Carbon sink capacity quantification and bottleneck diagnosis: Using the dynamic carbon sink model, combined with the sediment carbon burial rate and the CO2 exchange flux at the sea-air interface, calculate the carbon sink contribution values of each habitat type, identify low-efficiency carbon sink areas through contribution value comparison, and analyze their environmental limiting factors; S5. Enhanced sink technology integration and path generation: For the identified low-efficiency carbon sink areas, design habitat-specific enhanced sink solutions, including optimization of salt marsh vegetation communities, artificial replanting of seagrass beds, laying of algae attachment bases, and sediment improvement.

[0006] Optionally, the multi-source data fusion and habitat zoning in S1 include: S11. Satellite remote sensing data acquisition and processing: Obtain remote sensing image data of the target island through satellite remote sensing technology, including images in visible light bands, near-infrared bands, and infrared bands; S12. UAV scanning data acquisition: Scan the surface of the island through the multispectral camera equipped on the UAV to obtain ground image data. Among them, the scanning range of the UAV covers the island land vegetation area, intertidal salt marsh area, seagrass bed, and algal field area; S13. LiDAR data acquisition: Use LiDAR technology to scan the topography of the island to obtain three-dimensional topographic data, including the ground height, slope, and structural information of the island; S14. Cloud removal processing: Use a cloud removal algorithm to perform cloud removal processing on the remote sensing image data to remove the noise generated due to cloud cover; S15. Band fusion: Fuse remote sensing images in different bands through a multi-band fusion algorithm (principal component analysis PCA); S16. Spatial registration: Register satellite remote sensing data with LiDAR data and UAV scanning data through spatial registration technology (registration algorithm based on mutual information); S17. Habitat type zoning: Use the K-means clustering algorithm to divide the habitat types of the remote sensing image after cloud removal processing, and divide it into island land vegetation area, intertidal salt marsh area, shallow sea seagrass bed, and algal field area according to the topography, vegetation, and salt marsh characteristics of the island; S18. Output of habitat zoning results: Output the habitat type zoning map of the island, including the island land vegetation area, intertidal salt marsh area, shallow sea seagrass bed, and algal field area.

[0007] Optionally, the stratified sampling and measured carbon storage in S2 include: S21, Habitat stratification and sampling point layout: Based on the island habitat type zoning map, a stratified sampling principle was adopted to layout a number of sampling points in each habitat type according to area, representativeness and accessibility; S22, sample collection and testing: At each sampling point, targeted sample collection work is carried out according to different habitat characteristics. In particular, aboveground biomass and underground root samples of vegetation are collected from land and salt marsh areas, and sediment column samples and living seaweed / algae samples are collected from intertidal zones and shallow sea areas. The carbon content of the samples is determined by laboratory analysis methods (element analyzer) to obtain the carbon concentration of biological tissues and the carbon content of sediments; S23, calculation of carbon storage parameters and construction of database: Based on the obtained carbon concentration of biological tissues and carbon content of sediments, the carbon density of biomass per unit area, sediment carbon flux and growth cycle parameters of species under each habitat type are calculated respectively to form structured carbon storage data. The structured carbon storage data are then associated with the habitat type according to spatial coordinates to construct a standardized carbon storage measurement database.

[0008] Optionally, the habitat stratification and sampling point layout in S21 include: S211, Determination of stratification units: Based on the island habitat type zoning map, the study area was divided into several ecological stratification units, including island-terrestrial vegetation area, intertidal salt marsh area, shallow seagrass bed and algae field area; S212, quantification of indicators and setting of stratified sampling coefficients: Calculate the area indicator for each stratified unit, and combine the typical indicators (vegetation coverage, historical monitoring frequency) and accessibility indicators (traffic accessibility, water depth limit) to calculate the comprehensive score; S213, sampling point distribution planning and spatial balance optimization: According to the comprehensive score, a number of sampling points are arranged in proportion in each habitat type, and geographical locations with high comprehensive scores are given priority. The initial point plan is optimized using spatial balance constraints (minimum distance or grid method).

[0009] Optionally, the sample collection and detection in S22 includes: S221, Sample collection in different habitats: At each sampling point, carry out targeted sample collection according to the habitat type, including: Island vegetation area and intertidal salt marsh area: collect samples of aboveground biomass (such as stems and leaves) and underground root systems of vegetation; Intertidal zone and shallow sea area: use gravity sampler or vibrating tube sampling device to collect sediment column samples, and use scissors or suction nets to collect live samples of seagrass and algae; S222, carbon content experimental detection: the collected samples were pre-treated (air-dried, ground, sieved), and their carbon content was determined using an elemental analyzer (CHNS / O); S223, Carbon Concentration Calculation and Index Extraction: Based on the measured carbon content, calculate the carbon concentration of biological tissues and the carbon content of sediments.

[0010] Optionally, the carbon storage parameter calculation and database construction in S23 include: S231, Biomass Carbon Density Calculation: Based on the obtained carbon concentration values of biological tissues and the biomass data within the quadrat, calculate the biomass carbon density per unit area, which is used to represent the carbon sequestration capacity of vegetation or algal and seagrass communities under various habitat types; S232, Sediment Carbon Flux Calculation: Combine the sediment carbon content, sediment density, and annual burial rate to calculate the annual sediment carbon flux per unit area, reflecting the long-term carbon fixation capacity of the habitat unit; S233, Growth Cycle Parameter Extraction: Based on laboratory or field observation data, extract the growth cycle parameters of species (such as dominant algae, seagrasses), including annual net productivity, peak growth period, and carbon fixation time window, and record them in a unified format; S234, Database Construction: Bind the biomass carbon density, sediment carbon flux, growth cycle parameters with the spatial coordinates, habitat type, and collection time of the sampling points to form standardized structured entries, and write them into the measured carbon storage database.

[0011] Optionally, the construction of the dynamic carbon sequestration model in S3 includes: S31, Growth Cycle Environmental Simulation Experiment Design: Based on the habitat types and carbon absorption characteristics of each sampling point in the measured carbon storage database, select seagrass and algal species, set different gradients of light intensity, water temperature, and salinity in the laboratory-controlled environment, conduct full-growth cycle simulations, and record their net carbon absorption rates under different condition combinations; S32, Divide the experimental data by habitat type, and use a multiple regression model to establish the quantitative relationship between environmental factors (light, water temperature, salinity) and carbon absorption rate, forming a dynamic carbon sequestration model with habitat type as the classification unit.

[0012] Optionally, the quantification of carbon sequestration capacity and bottleneck diagnosis in S4 include: S41, Carbon Sequestration Contribution Value Calculation: Based on the dynamic carbon sequestration model, combine the sediment carbon burial rate and the CO2 exchange flux at the sea-air interface to calculate the total carbon sequestration capacity per unit area of each habitat type ; S42, Identification of Inefficient Carbon Sequestration Areas: Standardize and compare the carbon sequestration contribution values of all habitat units, set an inefficient threshold, and identify the habitat areas with low carbon sequestration capacity, expressed as: ; Wherein, is the set of identified inefficient carbon sequestration habitats, is the low - efficiency threshold coefficient (set to 0.3 - 0.5), is the maximum value among all habitat carbon sink values; S43, Environmental limiting factor analysis: Conduct correlation analysis or principal component analysis on the environmental parameter data (light, water temperature, salinity, sediment density) corresponding to each observation point in the low - efficiency carbon sink area to identify the dominant limiting factors.

[0013] Optionally, the enhancement technology integration and path generation in S5 include: S51, Classification of low - efficiency areas: According to the identified low - efficiency carbon sink areas, classify and group them according to their corresponding habitat types, and clarify the ecological type basis and intervenable factors in each area; S52, Formulation of enhancement technology integration plan: For different habitat types, design enhancement intervention plans with ecological adaptability and engineering feasibility, specifically including: For the intertidal salt marsh area, optimize the community structure of native salt - tolerant plants to increase above - ground / under - ground biomass; For the seagrass bed area, adopt the artificial ramet replanting technology to increase community density and coverage area; For the algal bed area, lay artificial attachment substrates with high attachment rates to promote the colonization and growth of algae; For areas with insufficient carbon burial capacity, improve carbon fixation conditions by means of sediment aeration, organic matter addition, or addition of particulate stabilizers.

[0014] Advantages of the present invention: In the present invention, by integrating satellite remote sensing, UAV multi - spectral images, and lidar topographic data, a multi - source information processing system with three - dimensional spatial resolution ability is constructed. After cloud removal processing, band fusion, spatial registration, and K - means clustering algorithm, it can accurately divide typical habitat types such as island land vegetation areas, intertidal salt marsh areas, shallow - sea seagrass beds, and algal bed areas, improving the automation recognition and classification efficiency of complex island ecosystems.

[0015] In the present invention, by constructing a closed - loop technology chain from stratified sampling, carbon content detection, carbon storage parameter extraction to dynamic carbon sink modeling, covering three core indicators of above - ground / under - ground biomass, sediment flux, and growth cycle, a quantitative relationship between environmental factors and carbon absorption rate is established through a habitat - specific regression model, realizing the dynamic simulation and spatial prediction of carbon sink capacity, and comprehensively reflecting the carbon sink response process of the ecosystem under different conditions.

[0016] In the present invention, by comparing and analyzing the carbon sink contribution values of each habitat and combining with environmental factor diagnosis, inefficient carbon sink areas are identified and their restrictive bottlenecks are clarified. Further, in combination with the habitat characteristics, differential carbon sink enhancement technologies including salt marsh vegetation optimization, seagrass replanting, attachment base laying, and sediment improvement are formulated, and a standardized and replicable technical path is formed, effectively enhancing the carbon sink enhancement potential of the overall ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a schematic flow diagram of the evaluation method for the embodiments of the present invention; Figure 2 It is a schematic diagram of hierarchical sampling and actual measurement of carbon storage for the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The present invention will be described in detail below in conjunction with the drawings and specific embodiments. At the same time, it should be noted here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well-known technologies, those skilled in the art can also adopt other alternative methods for implementation; moreover, the drawings are only for more specifically describing the embodiments and are not intended to specifically limit the present invention.

[0020] It should be pointed out that in the specification, when referring to "an embodiment", "embodiments", "exemplary embodiments", "some embodiments", etc., it indicates that the described embodiments may include specific features, structures or characteristics, but not necessarily every embodiment includes such specific features, structures or characteristics. Additionally, when combining embodiments to describe specific features, structures or characteristics, implementing such features, structures or characteristics in combination with other embodiments (whether explicitly described or not) should be within the knowledge scope of those skilled in the relevant art.

[0021] Generally, terms can be understood at least in part from their use in the context. For example, at least in part depending on the context, the term "one or more" used herein can be used to describe any feature, structure or characteristic in a singular sense, or can be used to describe a combination of features, structures or characteristics in a plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey a set of exclusive factors, but rather, at least in part depending on the context, allowing for the existence of other factors that may not be explicitly described.

[0022] Such as Figure 1 - Figure 2As shown in the figure, a method for monitoring and evaluating the carbon sink capacity of an island ecosystem includes the following steps: S1. Multi-source data fusion and habitat zoning: Obtain remote sensing images and three-dimensional terrain data of the island through satellite remote sensing, unmanned aerial vehicle (UAV) scanning, and lidar. Perform cloud removal, band fusion, and spatial registration on the remote sensing images, and divide the habitat type zoning map of the island. The habitat types include island land vegetation area, intertidal salt marsh area, shallow sea seagrass bed, and algal area; S2. Stratified sampling and actual measurement of carbon storage: Based on the habitat type zoning map of the island, lay out sampling points by stratifying according to habitat types, collect aboveground / underground biomass of vegetation, sediment column samples, and live seagrass / algae samples, measure the carbon content, and construct an actual measurement database of carbon storage including biomass carbon density, sediment carbon flux, and growth cycle parameters; S3. Construction of dynamic carbon sink model: Based on the actual measurement database of carbon storage, conduct full growth cycle environmental simulation experiments on seagrass and algae in different habitats, extract the correlation laws between light, water temperature, salinity, and carbon absorption rate, and establish a dynamic carbon sink model with habitat types as classification units; S4. Quantification of carbon sink capacity and bottleneck diagnosis: Use the dynamic carbon sink model, combined with the sediment carbon burial rate and the CO2 exchange flux at the sea-air interface, calculate the carbon sink contribution values of each habitat type, identify low-efficiency carbon sink areas through comparison of contribution values, and analyze their environmental limiting factors; S5. Integration of carbon sink enhancement technologies and path generation: For the identified low-efficiency carbon sink areas, design habitat-specific carbon sink enhancement schemes, including optimization of salt marsh vegetation communities, artificial replanting of seagrass beds, laying of algal attachment bases, and sediment improvement.

[0023] The multi-source data fusion and habitat zoning in S1 include: S11. Acquisition and processing of satellite remote sensing data: Obtain remote sensing image data of the target island through satellite remote sensing technology, including images in visible light band, near-infrared band, and infrared band; S12. Acquisition of UAV scanning data: Scan the surface of the island through the multi-spectral camera equipped on the UAV to obtain ground image data. Among them, the scanning range of the UAV covers the island land vegetation area, intertidal salt marsh area, seagrass bed, and algal area; S13. Acquisition of lidar data: Use lidar (LiDAR) technology to scan the terrain of the island to obtain three-dimensional terrain data, including ground height, slope, and structure information of the island; S14. Cloud removal processing: Use a cloud removal algorithm to perform cloud removal processing on the remote sensing image data to remove the noise generated due to cloud cover, expressed as: ; Among them, is the image data after cloud removal, is the original remote sensing image data, The part affected by clouds; The part affected by clouds Use multi - band threshold detection, expressed as: ; Wherein, , , are the reflectivities of the blue light, near - infrared, and short - wave infrared bands respectively, , is the reflectivity of the red light band; S15, Band fusion: Use the multi - band fusion algorithm (Principal Component Analysis PCA) to fuse remote sensing images of different bands to enhance the feature information in the images and improve the recognition ability of different habitats, expressed as: ; Wherein, is the fused band image, are the remote sensing image data of different bands respectively, and n is the number of bands; S16, Spatial registration: Use spatial registration technology (registration algorithm based on mutual information) to register satellite remote sensing data with lidar data and UAV scanning data so that they are aligned in spatial position, expressed as: ; Wherein, R is the registration transformation matrix, , are the pixel values of the two images respectively, and f is the image intensity function; ; Wherein, is the pixel point of the input image at coordinates (x, y), , , are the reflectivities or brightness values of the red, green, and blue bands at this point respectively; S17, Habitat type zoning: Use the K - means clustering algorithm to divide the cloud - removed remote sensing images into habitat types. According to the terrain, vegetation, and salt marsh characteristics of the island, it is divided into island - land vegetation area, intertidal salt marsh area, shallow - sea seagrass bed and algal area, expressed as: ; Wherein, D is the minimized distance metric, C is the cluster center, representing the characteristics of each habitat type, is the feature vector of the i - th sample, k is the index of the cluster, is the total number of samples participating in the clustering analysis, is the k - th clustering center; S18, Output of habitat zoning results: Output the habitat type zoning map of the island, including the island land vegetation area, intertidal salt marsh area, shallow sea seagrass bed and algal area.

[0024] The stratified sampling and actual measurement of carbon storage in S2 include: S21, Habitat stratification and sampling point layout: Based on the island habitat type zoning map, following the principle of stratified sampling, several sampling points are arranged in each habitat type according to area, representativeness and accessibility to ensure the balance and ecological representativeness of the sample spatial distribution; S22, Sample collection and detection: At each sampling point, targeted sample collection work is carried out according to different habitat characteristics. Among them, aboveground biomass and underground root samples of vegetation are collected in the land and salt marsh areas, sediment column samples and living seagrass / algae samples are collected in the intertidal zone and shallow sea areas, and the carbon content of the samples is measured by laboratory analysis methods (element analyzer) to obtain the carbon concentration of biological tissues and the sediment carbon content; S23, Calculation of carbon storage parameters and database construction: Based on the obtained carbon concentration of biological tissues and sediment carbon content, calculate the biomass carbon density per unit area, sediment carbon flux and growth cycle parameters of species under each habitat type respectively, form structured carbon storage data, and associate the structured carbon storage data with spatial coordinates and habitat types to construct a standardized actual measurement database of carbon storage.

[0025] The habitat stratification and sampling point layout in S21 include: S211, Determination of stratification units: Based on the island habitat type zoning map, the study area is divided into several ecological stratification units, including the island land vegetation area, intertidal salt marsh area, shallow sea seagrass bed and algal area; S212, Index quantification and setting of stratified sampling coefficient: Calculate the area index of each stratification unit respectively, and combine the typicality index (vegetation coverage, historical monitoring frequency) and accessibility index (traffic accessibility, water depth limit) to calculate the comprehensive score, expressed as: ; Among them, is the comprehensive score of the kth stratification unit, is the area normalization value, representing the relative area of this habitat unit in the entire study area, is the typicality index normalization value, combining vegetation coverage and historical monitoring frequency, is the accessibility index, considering traffic accessibility, water depth suitability, etc., defined as the reciprocal of the accessibility level or the normalization value after expert scoring, 、 、 are weight parameters; S213, sampling point distribution planning and spatial balance optimization: According to the comprehensive score, a number of sampling points are arranged in proportion in each habitat type, and geographical locations with high comprehensive scores are given priority. The initial point plan is optimized using spatial balance constraints (minimum distance or grid method) to ensure uniform distribution of sampling points in space and integrity of ecological coverage, expressed as: ; Where m is the number of sampling points set under the current habitat type, is the Euclidean distance from the i-th sampling point to its nearest neighbor sampling point, For all The average value of is a spatial balance evaluation index. The smaller the value, the more uniform the distribution of sampling points. By iteratively adjusting the initial sampling point set, By minimizing, the goal of spatial balance optimization can be achieved.

[0026] Sample collection and testing in S22 include: S221, Sample collection in different habitats: At each sampling point, carry out targeted sample collection according to the habitat type, including: Island vegetation area and intertidal salt marsh area: collect samples of aboveground biomass (such as stems and leaves) and underground root systems of vegetation; Intertidal zone and shallow sea area: use gravity sampler or vibrating tube sampling device to collect sediment column samples, and use scissors or suction nets to collect live samples of seagrass and algae; S222, Carbon content test: The collected samples are pre-treated (air-dried, ground, sieved), and their carbon content is determined using an elemental analyzer (CHNS / O). The test indicators for different sample types include: Vegetation and seagrass algae samples: Determine their carbon concentration per unit dry mass; Sediment samples: Determine the carbon content per unit dry mass; S223, Carbon concentration calculation and index extraction: Based on the measured carbon content, calculate the carbon concentration of biological tissues and the carbon content of sediments, expressed as: ; in, is the carbon concentration in biological tissues, is the carbon mass measured by element analyzer, is the dry weight of the biological sample; ; in, is the carbon content of the sediment, is the mass of carbon measured in the sediment sample, is the dry weight of sediment samples.

[0027] Calculation of carbon storage parameters and database construction in S23 include: S231, calculation of biomass carbon density: Based on the obtained carbon concentration values of biological tissues and biomass data within the quadrat, calculate the biomass carbon density per unit area, which is used to represent the carbon sequestration capacity of vegetation or algal and seagrass groups under various habitat types, expressed as: ; Among them, is the biomass carbon density per unit area, is the carbon concentration of biological tissues, is the dry biomass per unit area; S232, calculation of sediment carbon flux: Combine the sediment carbon content, sediment density, and annual burial rate to calculate the annual sediment carbon flux per unit area, which reflects the long-term carbon fixation ability of the habitat unit, expressed as: ; Among them, is the sediment carbon flux, is the sediment carbon content, is the dry density of the sediment, is the annual burial rate of the sediment; S233, extraction of growth cycle parameters: Based on laboratory or field observation data, extract the growth cycle parameters of species (such as dominant algae, seagrasses), including annual net productivity, peak growth period, carbon fixation time window, and record them in a unified format, expressed as: ; Among them, is the annual net productivity, is the net carbon fixation rate in the t-th observation time period, is the duration of each observation time period, and T is the number of time periods in a year, depending on the observation frequency (if weekly, then T = 52); ; Among them, is the time index of the peak growth period (such as the number of days or weeks); ; Among them, is the carbon fixation time window, is the indicator function, which is 1 if the condition is met, otherwise 0, is the carbon fixation rate threshold (set to 1.6 for island and continental vegetation areas, 0.825 for intertidal salt marsh areas, 0.7 for seagrass bed areas, and 0.7 for algal field areas in the morning); S234, Database construction: Bind the biomass carbon density, sediment carbon flux, growth cycle parameters with the spatial coordinates, habitat types, and collection times of sampling points to form standardized and structured entries, and write them into the measured carbon storage database.

[0028] The construction of the dynamic carbon sink model in S3 includes: S31, Design of growth cycle environmental simulation experiments: Based on the habitat types and carbon absorption characteristics of each sampling point in the measured carbon storage database, select seagrass and algal species, set different gradients of light intensity, water temperature, and salinity in a laboratory-controlled environment, conduct full-growth cycle simulations, and record their net carbon absorption rates under different combinations of conditions; S32, Divide the experimental data by habitat type, use a multiple regression model to establish the quantitative relationship between environmental factors (light, water temperature, salinity) and carbon absorption rate, and form a dynamic carbon sink model with habitat type as the classification unit, expressed as: ; Among them, is the predicted carbon absorption rate, h is the habitat type index, is the intercept term, , , are the regression coefficients, I is the light intensity, T is the water temperature, S is the current salinity, is the optimal salinity of the typical dominant species in this habitat.

[0029] The quantification of carbon sink capacity and bottleneck diagnosis in S4 includes: S41, Calculation of carbon sink contribution value: Based on the dynamic carbon sink model, combined with the sediment carbon burial rate and the sea-air interface CO2 exchange flux, calculate the total carbon sink capacity per unit area of each habitat type , expressed as: ; Among them, is the annual carbon sink contribution value per unit area of habitat type h, is the annual average biological carbon absorption rate obtained from the dynamic carbon sink model, is the length of one year, is the sediment carbon burial flux per unit area, is the sea-air interface per unit area net exchange flux (atmospheric absorption is positive); S42, Identification of low-efficiency carbon sink areas: Standardize and compare the carbon sink contribution values of all habitat units, set a low-efficiency threshold, and identify the habitat areas with low carbon sink capacity, expressed as: ; Among them, The set of identified low - efficiency carbon sink habitats is the low - efficiency threshold coefficient (set to 0.3 - 0.5), is the maximum value among all habitat carbon sink values; S43, Environmental limiting factor analysis: Conduct correlation analysis or principal component analysis on the environmental parameter data (light, water temperature, salinity, sediment density) corresponding to each observation point in the low - efficiency carbon sink area to identify the dominant limiting factor, expressed as: ; Among them, is the environmental factor and the carbon sink value of the Pearson correlation coefficient, is the i - th environmental factor, Cov is the covariance function, 、 are respectively 、 standard deviations.

[0030] The integration of carbon sink - increasing technologies and path generation in S5 includes: S51, Classification of low - efficiency areas: According to the identified low - efficiency carbon sink areas, classify and group them according to their corresponding habitat types, and clarify the ecological type basis and intervenable factors in each area; S52, Formulation of carbon sink - increasing technology integration plan: For different habitat types, design carbon sink - increasing intervention plans with ecological adaptability and engineering feasibility, specifically including: For the intertidal salt marsh area, optimize the community structure of native salt - tolerant plants and increase above - ground / under - ground biomass; For the seagrass bed area, adopt the artificial ramet replanting technology to increase community density and coverage area; For the algal field area, lay artificial attachment substrates with high attachment rates to promote algal colonization and growth; For areas with insufficient carbon burial capacity, improve carbon fixation conditions by means of sediment aeration, organic matter addition, or addition of particulate stabilizers.

[0031] This invention covers any substitutions, modifications, equivalent methods, and solutions made within the essence and scope of this invention. For the public to have a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments of this invention. However, those skilled in the art can also fully understand this invention without the description of these details. Additionally, well - known methods, processes, procedures, components, and circuits are not described in detail to avoid unnecessary confusion to the essence of this invention.

[0032] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A method for monitoring and evaluating the carbon sink capacity of an island ecosystem, characterized in that: The following steps are involved: S1, Multi-source data fusion and habitat zoning: Obtain remote sensing images and three-dimensional terrain data of the island through satellite remote sensing, drone scanning and lidar, remove cloud, fuse bands and perform spatial registration on the remote sensing images, and divide the island habitat type zoning map. The habitat types include island-land vegetation area, intertidal salt marsh area, shallow sea grass bed and algae field area; S2, stratified sampling and carbon storage measurement: Based on the island habitat type zoning map, sampling points are arranged in layers according to habitat type, and aboveground / underground vegetation biomass, sediment column samples and living seaweed / algae samples are collected to determine the carbon content. A carbon storage measurement database including biomass carbon density, sediment carbon flux and growth cycle parameters is constructed; S3, Construction of dynamic carbon sink model: Based on the measured carbon storage database, full growth cycle environmental simulation experiments were conducted on seagrasses and algae in different habitats to extract the correlation between light, water temperature, salinity and carbon absorption rate, and to establish a dynamic carbon sink model with habitat type as the classification unit; S4, Quantification of carbon sink capacity and bottleneck diagnosis: Using a dynamic carbon sink model, combined with sediment carbon burial rate and sea-air interface CO2 exchange flux, calculate the carbon sink contribution value of each habitat type, identify inefficient carbon sink areas through contribution value comparison, and analyze their environmental limiting factors; S5, Integration of carbon sink enhancement technologies and path generation: Design habitat-specific carbon sink enhancement plans for identified inefficient carbon sink areas, including salt marsh vegetation community optimization, artificial replanting of seagrass beds, laying of algae attachment bases and sediment improvement.

2. The method for monitoring and evaluating the carbon sink capacity of an island ecosystem according to claim 1, characterized in that: The multi-source data fusion and habitat partitioning in S1 include: S11, satellite remote sensing data acquisition and processing: obtain remote sensing image data of the target island through satellite remote sensing technology, including images of visible light band, near infrared band and infrared band; S12, UAV scanning data acquisition: The surface of the island is scanned by a multispectral camera equipped by the UAV to obtain ground image data, wherein the scanning range of the UAV covers the island vegetation area, intertidal salt marsh area, seagrass bed and algae field area; S13, LiDAR data acquisition: Use LiDAR technology to scan the island terrain and obtain three-dimensional terrain data, including the ground height, slope and structure information of the island; S14, cloud removal processing: use cloud removal algorithm to perform cloud removal processing on remote sensing image data to remove noise caused by the influence of clouds; S15, band fusion: multiple band fusion algorithms are used to fuse remote sensing images of different bands; S16, spatial registration: satellite remote sensing data is registered with lidar data and drone scanning data through spatial registration technology; S17, habitat type zoning: The K-means clustering algorithm is used to divide the habitat types of the remote sensing images after cloud removal. According to the topography, vegetation and salt marsh characteristics of the island, it is divided into island vegetation area, intertidal salt marsh area, shallow sea grass bed and algae field area; S18, output of habitat zoning results: output the habitat type zoning map of the island, including island-land vegetation area, intertidal salt marsh area, shallow sea grass bed and algae field area.

3. The method for monitoring and evaluating the carbon sink capacity of an island ecosystem according to claim 1, characterized in that: The stratified sampling and carbon storage measurement in S2 include: S21, Habitat stratification and sampling point layout: Based on the island habitat type zoning map, a stratified sampling principle was adopted to layout a number of sampling points in each habitat type according to area, representativeness and accessibility; S22, sample collection and testing: At each sampling point, targeted sample collection work is carried out according to different habitat characteristics. In particular, aboveground biomass and underground root samples of vegetation are collected from land and salt marsh areas, and sediment column samples and living seaweed / algae samples are collected from intertidal zones and shallow sea areas. The carbon content of the samples is determined by laboratory analysis methods to obtain the carbon concentration of biological tissues and the carbon content of sediments; S23, calculation of carbon storage parameters and construction of database: Based on the obtained carbon concentration of biological tissues and carbon content of sediments, the carbon density of biomass per unit area, sediment carbon flux and growth cycle parameters of species under each habitat type are calculated respectively to form structured carbon storage data. The structured carbon storage data are then associated with the habitat type according to spatial coordinates to construct a standardized carbon storage measurement database.

4. The method for monitoring and evaluating the carbon sink capacity of an island ecosystem according to claim 3, characterized in that: The habitat stratification and sampling point layout in S21 include: S211, Determination of stratification units: Based on the island habitat type zoning map, the study area was divided into several ecological stratification units, including island-terrestrial vegetation area, intertidal salt marsh area, shallow seagrass bed and algae field area; S212, quantification of indicators and setting of stratified sampling coefficients: Calculate the area indicator for each stratified unit, and calculate the comprehensive score by combining the typicality indicator and accessibility indicator; S213, sampling point distribution planning and spatial balance optimization: According to the comprehensive score, a number of sampling points are arranged in proportion in each habitat type, geographical locations with high comprehensive scores are given priority, and the initial point location plan is optimized using spatial balance constraints.

5. The method for monitoring and evaluating the carbon sink capacity of an island ecosystem according to claim 4, characterized in that: The sample collection and detection in S22 includes: S221, Sample collection in different habitats: At each sampling point, carry out targeted sample collection according to the habitat type, including: Island vegetation area and intertidal salt marsh area: collect samples of aboveground biomass and underground root systems of vegetation; Intertidal zone and shallow sea area: use gravity sampler or vibrating tube sampling device to collect sediment column samples, and use scissors or suction nets to collect live samples of seagrass and algae; S222, carbon content experimental detection: pre-process the collected samples and use an element analyzer to determine their carbon content; S223, Carbon concentration calculation and index extraction: Based on the measured carbon content, calculate the carbon concentration of biological tissues and the carbon content of sediments.

6. The method for monitoring and evaluating the carbon sink capacity of an island ecosystem according to claim 5, characterized in that: The carbon storage parameter calculation and database construction in S23 include: S231, calculation of biomass carbon density: based on the obtained carbon concentration values ​​of biological tissues and the biomass data in the sample plot, calculate the biomass carbon density per unit area to represent the carbon sink capacity of vegetation or algae and grass communities in each habitat type; S232, sediment carbon flux calculation: Combine sediment carbon content, sediment density and annual burial rate to calculate the annual sediment carbon flux per unit area, reflecting the long-term carbon fixation capacity of the habitat unit; S233, Growth cycle parameter extraction: Based on laboratory or field observation data, extract the growth cycle parameters of the species, including annual net productivity, peak growth period, carbon fixation time window, and record them in a unified format; S234, database construction: Bind biomass carbon density, sediment carbon flux, growth cycle parameters with the spatial coordinates of the sampling points, habitat type, and collection time to form standardized structured entries, and write them into the carbon storage measurement database.

7. The method for monitoring and evaluating the carbon sink capacity of an island ecosystem according to claim 6, characterized in that: The construction of the dynamic carbon sink model in S3 includes: S31, Design of growth cycle environment simulation experiment: Based on the habitat types and carbon absorption characteristics of each sampling point in the carbon storage measurement database, seagrass and algae species were selected, and different gradients of light intensity, water temperature and salinity were set in the laboratory controlled environment to simulate the whole growth cycle and record their net carbon absorption rate under different combinations of conditions; S32, the experimental data were divided according to habitat type, and the quantitative relationship between environmental factors and carbon absorption rate was established using a multivariate regression model to form a dynamic carbon sink model with habitat type as the classification unit.

8. The method for monitoring and evaluating the carbon sink capacity of an island ecosystem according to claim 1, characterized in that: The carbon sequestration capacity quantification and bottleneck diagnosis in S4 include: S41, Calculation of carbon sink contribution: Based on the dynamic carbon sink model, combined with the sediment carbon burial rate and the sea-air interface CO2 exchange flux, calculate the total carbon sink capacity per unit area of ​​each habitat type ; S42, Identification of inefficient carbon sink areas: Standardize and compare the carbon sink contribution values ​​of all habitat units, set an inefficient threshold, and identify habitat areas with low carbon sink capacity, expressed as: ； in, For the identified inefficient carbon sink habitat set, is the inefficient threshold coefficient, It is the maximum carbon sink value among all habitats; S43, Environmental limiting factor analysis: Conduct correlation analysis or principal component analysis on the environmental parameter data corresponding to each observation point in the inefficient carbon sink area to identify the dominant limiting factors.

9. The method for monitoring and evaluating the carbon sink capacity of an island ecosystem according to claim 1, characterized in that: The integration of sink enhancement technologies and path generation in S5 include: S51, Classification of inefficient areas: Based on the identified inefficient carbon sink areas, they are classified and grouped according to their corresponding habitat types, and the ecological type basis and intervention factors of each area are clarified; S52, formulation of integrated carbon sink enhancement technology plan: Design carbon sink enhancement intervention plans with ecological adaptability and engineering feasibility for different habitat types, including: For intertidal salt marsh areas, optimize the community structure of local salt-tolerant plants and increase aboveground / underground biomass; For seagrass beds, artificial division and replanting techniques are used to increase community density and coverage area; For algae fields, artificial attachment substrates with high attachment rates are laid to promote algae colonization and growth; For areas with insufficient carbon burial capacity, sediment aeration, organic matter addition, or particle stabilizer addition are used to improve carbon fixation conditions.

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